CN120869163B - Space positioning method and system for four-foot inspection robot based on gravity center sensing - Google Patents

Abstract

This invention discloses a spatial positioning method and system for a quadrupedal inspection robot based on center of gravity perception. The method aims to construct a center of gravity potential energy field using foot contact forces and body posture information, and combine this with wave spectrum analysis to form a center of gravity disturbance pattern, revealing the disturbance propagation mechanism. It employs inverse compensation technology to generate an anti-disturbance sequence, reconstructing the foot force distribution topology to achieve active disturbance suppression. Multi-frequency probe beams and coherent superposition technology are introduced to generate an environmental hologram and reconstruct the geometric topology, establishing a high-precision environmental perception system. Energy density analysis identifies high and low impedance regions, and a dual strategy of potential field circumvention and streamline tracing is used to construct the energy-optimal path. An oscillation analysis space is established for path projection simulation to identify resonance risk points and determine stabilization parameters. Finally, a quantum entanglement-based multi-anchor synchronization mechanism is used to generate a coordinate reference, fusing environmental geometric information to achieve precise positioning, thus realizing a closed-loop technology from center of gravity dynamics analysis to environmental perception navigation.

Description

Space positioning method and system for four-foot inspection robot based on gravity center sensing

Technical Field

The invention relates to the technical field of robot control, in particular to a space positioning method and system of a four-foot inspection robot based on gravity center sensing.

Background

The four-foot robot is taken as an important branch of the bionic robot, and stable walking and accurate navigation under a complex terrain environment are all the core technical challenges in the field. The existing four-legged robot control system mainly depends on the traditional gait planning and feedback control method, and can realize basic walking functions, but when facing dynamic disturbance and complex environments, the existing four-legged robot control system often lacks deep understanding on a gravity disturbance propagation mechanism, and active disturbance compensation and predictive balance adjustment are difficult to realize.

The current environment sensing and path planning technology mostly adopts schemes such as a laser radar and a visual SLAM, and the like, and can construct an environment map, but the problems of limited sensing precision, high calculation complexity, sensitivity to illumination conditions and the like exist, and particularly under the conditions of dynamic environment and complex terrain, high-precision real-time positioning and optimal path planning are difficult to realize. In addition, the existing method lacks systematic analysis of the coupling relation between the gravity center dynamic characteristic and the environment of the robot, and cannot establish organic connection among disturbance prediction, path optimization, oscillation suppression and accurate positioning, so that the overall control performance is limited, and the requirement of long-term stable operation of the quadruped robot under a severe environment is difficult to meet.

Disclosure of Invention

The invention discloses a space positioning method and a system of a quadruped inspection robot based on barycenter sensing, which aim to construct a barycenter disturbance propagation model through barycenter potential energy field analysis and fluctuation spectrum fusion to realize active disturbance compensation, combine multi-frequency holographic detection and geometric topology reconstruction technology to establish a high-precision environment sensing system, generate an energy optimal navigation track through energy density analysis and dual-path fusion strategy, determine system stabilization parameters through an oscillation analysis space projection and resonance risk identification method, and finally realize centimeter-level accurate positioning based on quantum entanglement coordinate synchronization and geometric fusion calibration to provide a complete technical solution for stable control and autonomous navigation of the quadruped robot in a complex dynamic environment.

The invention provides a space positioning method of a four-foot inspection robot based on gravity center sensing, which comprises the following steps:

Acquiring contact force and body attitude angles of each foot of the four-foot robot, constructing a gravity potential energy field based on the contact force, generating a fluctuation spectrum based on the body attitude angles, and coupling the gravity potential energy field and the fluctuation spectrum to form a gravity disturbance mode;

Analyzing a disturbance propagation path from the gravity center disturbance mode, performing reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence, and reconstructing a foot end force distribution topology based on the anti-disturbance sequence;

Establishing a dynamic coordinate anchor point based on the foot end force distribution topology, transmitting a multi-frequency detection beam from the dynamic coordinate anchor point to an environment space, performing coherent superposition on the multi-frequency detection beam to generate a hologram, and reconstructing an environment geometric topology by using the hologram;

Performing energy density analysis in the environment geometric topology to identify a high-impedance area and a low-impedance area, performing potential field detouring on the high-impedance area to form a potential energy track, performing streamline tracking on the low-impedance area to form an energy flow track, and constructing an energy optimal path based on the potential energy track and the energy flow track;

Constructing an oscillation analysis space based on the gravity center disturbance mode, projecting the energy optimal path to the oscillation analysis space for simulation to generate a gravity center resonance mode, identifying resonance risk points from the gravity center resonance mode, and determining a stabilization parameter by utilizing the resonance risk points;

and carrying out quantum entanglement type synchronization on the dynamic coordinate anchor points based on the stabilization parameters to generate coordinate references, and carrying out fusion calibration on the coordinate references and the environment geometric topology to generate positioning coordinates.

The second aspect of the invention provides a space positioning system of a four-foot inspection robot based on gravity sensing, which comprises:

The gravity sensing module is used for acquiring contact force and body attitude angles of each foot of the four-foot robot, constructing a gravity potential energy field based on the contact force, generating a fluctuation frequency spectrum based on the body attitude angles, and coupling the gravity potential energy field and the fluctuation frequency spectrum to form a gravity disturbance mode;

The disturbance compensation module is used for analyzing a disturbance propagation path from the gravity center disturbance mode, carrying out reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence, and reconstructing a foot end force distribution topology based on the anti-disturbance sequence;

The environment modeling module is used for establishing a dynamic coordinate anchor point based on the foot end force distribution topology, transmitting a multi-frequency detection beam from the dynamic coordinate anchor point to an environment space, performing coherent superposition on the multi-frequency detection beam to generate a hologram, and reconstructing an environment geometric topology by using the hologram;

The path planning module is used for carrying out energy density analysis in the environment geometric topology to identify a high-impedance area and a low-impedance area, carrying out potential field detouring on the high-impedance area to form a potential energy track, carrying out streamline tracking on the low-impedance area to form an energy flow track, and constructing an energy optimal path based on the potential energy track and the energy flow track;

The resonance analysis module is used for constructing an oscillation analysis space based on the gravity center disturbance mode, projecting the energy optimal path to the oscillation analysis space to simulate and generate a gravity center resonance mode, identifying resonance risk points from the gravity center resonance mode, and determining a stabilization parameter by utilizing the resonance risk points;

And the positioning generation module is used for carrying out quantum entanglement type synchronous generation on the dynamic coordinate anchor points based on the stabilization parameters to generate coordinate references, and carrying out fusion calibration on the coordinate references and the environment geometric topology to generate positioning coordinates.

Firstly, through the gravity center potential energy field construction and fluctuation spectrum fusion technology, the inherent association mechanism of foot-end contact force and organism attitude angle is deeply excavated, the analysis result of the disturbance propagation path is converted into an accurate anti-disturbance sequence by adopting a reverse compensation strategy, the dynamic reconstruction of foot-end force distribution topology is realized, the control strategy can be actively adjusted before disturbance occurs, and the traditional responsive balance control is converted into predictive active control. And secondly, reconstructing an environment geometric topology by adopting a coherent superposition mechanism of multi-frequency detection beams, decoupling an environment space into a high-impedance area and a low-impedance area by energy density analysis, and constructing an energy optimal track by utilizing a dual-path strategy of potential field detour and streamline tracking, so that the deep fusion of environment perception precision and path planning efficiency is realized, and the robot can simultaneously meet the double requirements of safety and energy consumption optimization in complex terrains. And finally, establishing an oscillation analysis space projection mechanism, combining path information with gravity center resonance characteristics to perform resonance risk identification, realizing oscillation suppression through dynamic adjustment of stabilization parameters, constructing a unified coordinate reference by combining a quantum entanglement type multi-anchor point synchronization technology, generating high-precision positioning coordinates by fusing environment geometric constraint information, and forming a closed-loop control system from disturbance prediction, path optimization to precise positioning.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles and effects of the invention.

Unless otherwise indicated, the same reference numerals in different drawings denote the same or similar technical features, and different reference numerals may be used for the same or similar technical features.

Fig. 1 is a schematic flow chart of a space positioning method of a four-foot inspection robot based on gravity sensing.

Fig. 2 is a block diagram of a space positioning system of a four-foot inspection robot based on barycenter perception.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The following describes the technical scheme of the embodiment of the application.

As shown in fig. 1, the embodiment of the invention provides a space positioning method of a four-foot inspection robot based on gravity center sensing, which comprises the following steps of S110 to S160:

step S110, the contact force and the body attitude angle of each foot of the four-foot robot are obtained, a gravity potential energy field is constructed based on the contact force, a fluctuation frequency spectrum is generated based on the body attitude angle, and the gravity potential energy field and the fluctuation frequency spectrum are coupled to form a gravity disturbance mode.

Specifically, the foot contact force and the body attitude angle of each foot of the four-foot robot are obtained. The foot contact force is acquired in real time through six-dimensional force sensors arranged at the foot ends, the sensor type adopts strain gauge force/moment sensors, the measurement precision reaches +/-0.1N, and the sampling frequency is set to be 1000Hz. The four foot ends are respectively marked as RF (right front foot), LF (left front foot), RH (right rear foot) and LH (left rear foot), and each foot end acquires three-dimensional contact force Fx, fy and Fz and three-dimensional moment Mx, my and Mz. The sensor mounting position is positioned at the center of the foot end, which is contacted with the ground, and the mounting mode adopts rigid connection to ensure the accuracy of force transmission. The attitude angle of the machine body is obtained through an Inertial Measurement Unit (IMU) arranged at the gravity center position of the machine body, and comprises a Roll angle Roll, a Pitch angle Pitch and a Yaw angle Yaw, wherein the angle measurement precision is +/-0.1 degrees, and the output frequency is 1000Hz. The IMU coordinate system coincides with the machine body coordinate system, the x-axis is directed to the front of the machine body, the y-axis is directed to the left side of the machine body, and the z-axis is vertically upwards. The data preprocessing comprises zero calibration, temperature compensation and filtering noise reduction, and a 5-order Butterworth low-pass filter is adopted, the cut-off frequency is set to be 50Hz, and high-frequency noise interference is eliminated. The zero calibration of the force sensor is carried out in a suspended state of the robot, so that the influence of the dead weight and the installation stress of the sensor is eliminated.

A gravity potential energy field is constructed based on the contact force. And taking a supporting polygon of the four-foot robot as a definition domain of a potential energy field, wherein the supporting polygon is determined by a convex hull formed by current grounding foot end coordinates. The support polygon is triangular for the three-foot support state and quadrangular for the four-foot support state. The potential energy field strength is proportional to the vertical contact force of each foot end, and the potential energy value P (x, y) =Σ (Fi. Wi (x, y)), wherein Fi is the vertical contact force of the ith foot end, wi (x, y) is a space weight function, and a Gaussian kernel function form is adopted. The radius of influence of the weighting function is determined according to the foot end spacing, and is generally set to 50% of the foot end spacing, typically 20-30cm. The resolution of the potential energy field grid is set to be 2cm x 2cm, and the supporting polygon and the 20 cm-outward expansion area thereof are covered, so that all possible gravity center positions are ensured to be contained. The potential energy gradient calculation is used for identifying the change direction and the strength of the potential energy field, the partial derivative is calculated by adopting a numerical difference method, and the region with large gradient represents poor gravity center stability. And identifying potential energy extreme points through the condition that the gradient is zero, precisely positioning by adopting an iterative optimization method, wherein the local minimum value point corresponds to a stable balance position, and the local maximum value point corresponds to an unstable balance position. The boundary condition of the potential energy field is set as a free boundary where the potential energy value is extrapolated linearly to zero.

A fluctuation spectrum is generated based on the body posture angle. And carrying out spectrum analysis on the acquired attitude angle time sequence, and converting the time domain signal into a frequency domain by adopting a fast Fourier transform technology. The analysis window was set to 2 seconds, including 2048 samples, and the overlap ratio was 50%, ensuring the continuity and accuracy of the spectrum analysis. The window function reduces the frequency spectrum leakage by adopting a hanning window, and the length of the window function is the same as that of the analysis window. Fluctuation spectrum S (f) = |fft (θ (t))| ², where θ (t) is the attitude angle time series and f is the frequency. The frequency range of main concern is 0.1-20Hz, covering the main frequency components of the robot walking, running, jumping and other motion modes. The low frequency band (0.1-2 Hz) corresponds to the overall motion mode of the robot, the medium frequency band (2-10 Hz) corresponds to foot-to-foot impact and joint vibration, and the high frequency band (10-20 Hz) corresponds to structural vibration and sensor noise. The frequency spectrum peak value identifies and extracts the dominant frequency of the gesture fluctuation, and the peak detection standard is that the amplitude exceeds the frequency spectrum mean value by more than 2 times and the peak width is more than 0.5Hz. The fluctuation characteristic parameters comprise dominant frequency, spectrum bandwidth, spectrum center of gravity and the like, and reflect the time-frequency characteristic of the fluctuation of the robot gesture. The frequency spectrums of the multi-axis gestures form a comprehensive frequency spectrum through weighted combination, the weight is determined according to the variance of the gesture change of each axis, the roll angle weight and the pitch angle weight are large, and the yaw angle weight is small.

In some embodiments, the coupling of the gravity potential energy field and the fluctuation frequency spectrum to form a gravity disturbance mode comprises locating a potential energy extreme point in the gravity potential energy field, generating a coupling frequency based on frequency matching of the potential energy extreme point and the fluctuation frequency spectrum, exciting potential energy field oscillation by using the coupling frequency to generate a disturbance wave, and forming the gravity disturbance mode based on the disturbance wave.

And positioning potential energy extreme points in the gravity potential energy field. And identifying the extreme point position by calculating the first and second partial derivatives of the potential energy field, wherein the point of which the first partial derivative is zero and the characteristic value of the second partial derivative matrix meets a specific condition is the extreme point. The numerical calculation adopts a differential method to improve the precision, and the boundary points adopt forward or backward differential. The extreme point classification includes stable extreme points (local minimum, positive second partial derivative matrix), unstable extreme points (local maximum, negative second partial derivative matrix), saddle points (second partial derivative matrix is not constant, neither maximum nor minimum). The potential energy intensity of the extreme point is calculated through the difference value between the potential energy intensity of the extreme point and the background potential energy, the background potential energy is defined as the average value of potential energy in a field, and the extreme point with large intensity has obvious influence on the stability of the heart. The spatial distribution characteristic analysis of the extreme points identifies the topological structure of the potential energy field, and the topological structure comprises geometric characteristics such as the number of the extreme points, relative positions, connecting line angles and the like. There are typically 1-3 stable extrema and 0-2 unstable extrema within a single support polygon, the specific number depending on the uniformity of the foot end force distribution. The intensity levels of the extreme points are divided into three levels of a strong extreme point, a medium extreme point and a weak extreme point.

And performing frequency matching on the basis of the potential energy extreme points and the fluctuation frequency spectrum to generate coupling frequency. The spatial characteristics of potential energy extreme points are converted into frequency characteristics, and the conversion method is based on the corresponding relation between the distance between the extreme points and the wavelength. The relation between the spacing of extreme points and the corresponding frequency is established through disturbance propagation speed, the propagation speed is determined according to the dynamic parameters of the robot, and the typical value is 0.5-2.0m/s. The propagation speed is related to the mass, rigidity and damping characteristics of the robot, and the propagation speed of the robot with large mass is slower. The frequency matching adopts a nearest neighbor method, and a peak frequency closest to the extreme point frequency is found in the fluctuation frequency spectrum, wherein the search range is +/-20% of the extreme point frequency. The matching tolerance is set to be +/-10%, and frequencies beyond the tolerance range do not participate in coupling, so that interference of irrelevant frequencies is avoided. The coupling strength is determined by the product of the frequency matching and the potential energy strength. The extreme points may correspond to the same frequency, where the coupling strengths are superimposed, but the maximum coupling strength limit is set to prevent excessive coupling. The priority of the coupling frequencies is ordered according to the coupling strength, and meanwhile the stability type of the extreme points is considered, wherein the stability extreme points are higher than the instability extreme points in priority.

The method for generating the disturbance wave by exciting the potential energy field oscillation by using the coupling frequency comprises the steps of starting potential energy field initial oscillation based on the coupling frequency, accelerating and amplifying the potential energy field initial oscillation to form intensified oscillation, controlling the amplitude of the intensified oscillation to generate controlled oscillation, and braking and stabilizing the controlled oscillation to generate the disturbance wave.

Potential energy field initial oscillation is initiated based on the coupling frequency. And applying periodic disturbance force to the identified potential energy extreme point, wherein the disturbance frequency is equal to the coupling frequency, and the disturbance direction is along the potential energy gradient direction. The initial oscillation amplitude is set to be 5% of the potential energy field strength, so that the disturbance can not excessively influence the stability of the system, and the initial disturbance force is about 10-50N for a typical medium-sized four-legged robot. The spatial distribution of the disturbance force adopts Gaussian distribution, the center is positioned at an extreme point, and the standard deviation is equal to the size (2 cm) of one grid unit, so that a localized disturbance source is formed. The time function of the disturbance force takes the form of a sine. The initial oscillation phase relation of a plurality of extreme points is determined according to spatial arrangement, the phase difference of adjacent extreme points is pi/2, a traveling wave propagation mode is formed, and random phases can be set for the remote extreme points to avoid over-synchronization. The oscillation starting adopts a soft starting mode, the disturbance amplitude is smoothly increased to a set value within 0.5 seconds, and high-frequency impact generated by step input is avoided. The number of disturbance sources is usually limited to less than 3, so that complex interference phenomena generated by excessive disturbance sources are avoided.

And (5) carrying out acceleration amplification on the initial oscillation of the potential energy field to form intensified oscillation. When the disturbance frequency approaches to the natural frequency of the potential energy field, a resonance phenomenon occurs, and the oscillation amplitude rapidly increases exponentially. The natural frequency of the potential energy field is determined through linearization analysis, and the resonance frequency is related to the rigidity coefficient of the potential energy field and the equivalent mass of the robot. The stiffness coefficient is highly related to the foot contact stiffness and the center of gravity of the robot, typically 10 ⁴-10⁶ N/m. Resonance detection is judged by the rate of increase of the oscillation amplitude, which exceeds 10%/second and the duration is greater than 0.2 seconds, which is considered to be in resonance. The quality factor Q determines the sharpness of resonance, with the larger the Q value, the more pronounced the resonance effect. The amplitude increase of the ringing follows the envelope modulation law of a (t) =a ₀ x [1+q×sin (2pi f_res×t) ] where a ₀ is the initial amplitude. The maximum multiple of the resonance amplification is set to 10 times, so that the system instability caused by the overlarge oscillation amplitude is prevented. In the multi-frequency resonance, each frequency component is amplified independently and then is superimposed linearly, but nonlinear coupling effect needs to be considered. The temperature drift and the load dependence of the resonance frequency are adaptively compensated by parameters, so that the stability of the resonance effect is maintained.

Amplitude control by means of intensified oscillations produces controlled oscillations. And a proportional-integral-derivative control algorithm is adopted to adjust the magnitude of the disturbing force in real time so as to control the oscillation amplitude to be within a preset range. The control parameters are adjusted according to the dynamic characteristics of the robot. The amplitude target value is set according to the gravity center disturbance requirement, the weak disturbance mode target value is 10% of the potential energy field strength, the medium disturbance mode is 20%, and the strong disturbance mode is 30%. The controller is digitally realized, the sampling period is the same as the sampling period of the force sensor (1 ms), and the real-time performance of the control is ensured. The integral term sets saturation limit to prevent integral saturation phenomenon, and the differential term adopts a first order filter to reduce noise influence. The dead zone of the amplitude control is set to be +/-2% of the target amplitude, so that the controller is prevented from frequent action around the target amplitude to generate chatter. During multi-axis coupling control, the axis controllers are independent of each other and share the amplitude target value, and coordination control is realized through cross coupling items. Steady state errors of the controlled oscillations are eliminated by integral control and transient response is improved by differential control.

Braking and stabilizing the controlled oscillation to generate disturbance waves. When the oscillation is stopped or the disturbance mode is switched, the disturbance frequency is gradually reduced, and the resonance condition is destroyed by frequency detuning so as to realize stable braking. The frequency attenuation adopts a linear attenuation mode, the attenuation rate is set to 5%/second, and the disturbance is stopped after the coupling frequency is linearly reduced to 0.1 Hz. The disturbance amplitude is reduced in the braking process, an exponential decay mode is adopted, the time constant is related to the damping characteristic of the robot, and the system with large damping decays fast. The braking strategy is divided into two modes, namely soft braking and hard braking, wherein the soft braking is used for normal mode switching, the hard braking is used for emergency stop, and the time constant of the hard braking is halved. The attenuation processes of frequency and amplitude are kept synchronous, transient impact is avoided, and the attenuation curve adopts an S-shaped function to ensure smooth transition. The finally formed disturbance wave has four stages of definite start, development, stabilization and attenuation, and the duration of each stage is set according to the control requirement. The frequency content of the disturbance wave is mainly concentrated near the coupling frequency, and the bandwidth is usually 10-20% of the center frequency. The criterion for braking completion is that the oscillation amplitude is less than 1% of the initial set point and the frequency falls below a threshold.

A gravity center disturbance mode is formed based on the disturbance wave. And superposing the generated disturbance wave on the static gravity center potential energy field to form dynamic gravity center disturbance distribution. The spatial characteristics of the disturbance modes are determined through the propagation and interference of disturbance waves, and waveforms generated by a plurality of disturbance sources are mutually overlapped to form a complex interference pattern. Disturbance pattern D (x, y, t) =p (x, y) +Σ (ai×sin (2pi fit+phi)), where P (x, y) is the static potential energy field, ai is the amplitude of the ith disturbance wave, fi is the frequency, and phi is the phase. The time evolution characteristics of the disturbance modes are divided into three types according to the phase relation of disturbance waves, wherein the periodic disturbance modes are formed by in-phase superposition, the quasi-periodic disturbance modes are formed by out-phase superposition, and the irregular disturbance modes are formed by random phases. The intensity distribution of the disturbance mode shows obvious spatial non-uniformity, the disturbance intensity of the area close to the extreme point is large, and the disturbance intensity of the area far away from the extreme point is small. The dominant frequency of the mode typically coincides with the coupling frequency of the input, but harmonic components and combined frequency components are generated due to nonlinear effects. The spatial wavelength of the disturbance mode is related to the extreme point spacing, and the typical wavelength is 10-50cm, and the corresponding frequency range is 1-10Hz. The intensity class of the disturbance mode is divided into weak disturbance (amplitude is smaller than 10% of static field intensity), medium disturbance (10-30%), strong disturbance (larger than 30%), and different intensity classes correspond to different gait adjustment strategies. The duration of the disturbance mode is set according to gait control requirements, short-term disturbance (1-2 seconds) is used for gait fine adjustment, and long-term disturbance (5-10 seconds) is used for gait adaptation training.

And step S120, analyzing a disturbance propagation path from the gravity center disturbance mode, performing reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence, and reconstructing a foot-end force distribution topology based on the anti-disturbance sequence.

Specifically, the disturbance propagation path is resolved from the barycentric disturbance pattern. The propagation direction V (x, y, t) = - ∇ D (x, y, t) is determined by calculating the gradient vector of the disturbance mode, the gradient points to the direction in which the disturbance intensity increases the fastest, and the negative gradient direction is the disturbance energy propagation direction. The propagation speed is calculated by the displacement of the disturbance peak position at the adjacent moment, and the propagation speed is equal to the ratio of the peak displacement distance to the time interval. The identification of the disturbance peak value adopts a local maximum value detection algorithm, and a detection threshold value is set to be 1.5 times of the mean value of the disturbance amplitude. The geometric characteristics of the propagation path include parameters such as path length, curvature, bifurcation point, etc., the path length is calculated by integrating arc length, and the curvature is defined as the ratio of the path length to the straight line distance of the starting point and the ending point. The propagation characteristics of the disturbance in different areas are different, the propagation speed is high in the areas with large potential energy gradient, the propagation speed is low in the potential energy flat areas, and the typical range of the propagation speed is 0.3-1.5m/s. The classification of propagation paths includes straight propagation (curvature less than 1.2), curved propagation (curvature 1.2-2.0), spiral propagation (curvature greater than 2.0 and existence of a gyration phenomenon).

And performing reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence. The core idea of the reverse compensation is to apply compensation disturbance with opposite phase and equal amplitude to the original disturbance at the starting point, the middle key point and the ending point of the disturbance propagation path. The compensation disturbance amplitude Ac= -alpha x Ad, where Ad is the original disturbance amplitude, alpha is the compensation coefficient (typical value 0.8-1.2), and the negative sign indicates opposite phase. The compensation delay is determined based on the propagation distance and the propagation speed, so that the compensation disturbance can reach the target position at a proper time. The selection of the key nodes is based on geometric characteristics of the propagation path, including a path start point, an inflection point, a bifurcation point, a convergence point and an end point, and each key node corresponds to a compensation action. The generation of the anti-disturbance sequence adopts a time sequence control method, and the compensation disturbance is arranged according to a time sequence to form a control sequence. The sequence elements comprise parameters such as compensation time, compensation position, compensation amplitude, compensation direction and the like, and each sequence element corresponds to a specific compensation action. When multipath compensation is performed, the compensation sequences of all paths are executed in parallel, and coordinated actions are ensured through a time synchronization mechanism. The priority setting of the compensation sequence is based on the importance of the perturbed paths, the compensation priority of the primary propagation path being highest and the secondary and branch paths being lower.

Reconstructing a foot-end force distribution topology based on the anti-disturbance sequence. The basic principle of foot end force reconstruction is that the shape of a gravity center potential energy field is changed by changing foot end force distribution, so that the corrected potential energy field can offset the influence of original disturbance. Foot force adjustment amount Δfi=ji×ac, where Ji is the jacobian element of the ith foot, and Ac is the target compensation disturbance vector. The jacobian matrix describes the mapping relation between the foot end force change and the gravity center position change, and is obtained through robot kinematics and statics analysis, and the typical value range of the matrix elements is 0.1-0.5. Reconstruction of the force distribution topology follows the static equilibrium constraint, Σ (Fi) =mg, Σ (ri×fi) =0, where Fi is the i-th foot end force vector, ri is the foot end position vector, mg is the robot gravity. The spatial characteristics of topology reconstruction are reflected by redistribution of foot end forces in the support polygon, and the original uniformly distributed foot end forces are unevenly adjusted according to anti-disturbance requirements. The force distribution adjusting modes comprise diagonal adjustment (simultaneous increase and decrease of the force at the foot end of the diagonal), lateral adjustment (the force at the foot end at the same side changes in the same direction) and fore-and-aft adjustment (the force at the foot end at the front and back changes differentially). The geometric representation of the topology takes the form of a vector field, where each foot end position is plotted against a force vector, the length of the vector representing the magnitude of the force and the direction of the force. The dynamic topology adjustment updates the foot force distribution in real time according to the time characteristic of the anti-disturbance sequence, the adjustment frequency is kept synchronous with the disturbance frequency, and the typical adjustment frequency is 10-100Hz. The adjustment of foot end force needs to meet the friction constraint condition, ensure that the tangential force does not exceed the maximum static friction force, and the typical value of the static friction coefficient is 0.6-0.8. The connectivity characteristics of the topology are reflected by the force transmission relation between the foot ends, the coupling transmission of force exists between the adjacent foot ends, and the coupling between the diagonal foot ends is weaker. The reconstructed force distribution topology presents obvious asymmetric characteristics, the asymmetry is the embodiment of the anti-disturbance function, and the active counteracting of disturbance is realized by destroying the original symmetric balance.

And step S130, a dynamic coordinate anchor point is established based on the foot end force distribution topology, a multi-frequency detection beam is emitted to the environment space from the dynamic coordinate anchor point, a hologram is generated by carrying out coherent superposition on the multi-frequency detection beam, and the environment geometric topology is reconstructed by utilizing the hologram.

In some embodiments, establishing the dynamic coordinate anchor point based on the foot end force distribution topology comprises determining anchor point accuracy based on the foot end force distribution topology evaluation establishment complexity including force distribution uniformity, topology stability and spatial symmetry, setting coordinate transformation parameters according to the anchor point accuracy, and establishing the dynamic coordinate anchor point by spatially transforming the foot end force distribution topology using the coordinate transformation parameters.

And determining anchor point precision based on the foot end force distribution topology evaluation and establishment complexity. The required precision level of anchor point establishment is determined by analyzing complexity indexes of three dimensions of force distribution uniformity, topological stability and spatial symmetry. The uniformity of force distribution is characterized by calculating the standard deviation of the force of each foot end, wherein the uniformity index U=1-sigma F/mu F, sigma F is the standard deviation of the force of the foot end, mu F is the average value of the force of the foot end, and the closer the U value is to 1, the more uniform the distribution is. The topological stability is assessed by analyzing the amplitude of the change in the force distribution over a time window, the stability index s=1- Δf/Fmax, where Δf is the amplitude of the force change and Fmax is the maximum foot end force. The spatial symmetry is quantified by calculating the degree of symmetry of the force distribution relative to the body centerline, symmetry index sym=1- |fl-fr|/(fl+fr), where FL and FR are the resultant force magnitudes of the left and right foot-end forces, respectively. The overall complexity index c=w1×u+w2×s+w3×sym, where w1, w2, w3 are weight coefficients, typically 0.4, 0.3. The anchor point precision grade is divided into three grades of high precision (C > 0.8), medium precision (C is more than or equal to 0.5 and less than or equal to 0.8) and low precision (C < 0.5). The position error of the high-precision anchor point is less than 1cm, the angle error is less than 1 degree, the position error of the medium-precision anchor point is less than 5cm, the angle error is less than 5 degrees, the position error of the low-precision anchor point is less than 10cm, and the angle error is less than 10 degrees.

And setting coordinate transformation parameters according to the anchor point precision. Based on the determined anchor point precision level, setting corresponding coordinate transformation matrix parameters and calculation precision. The coordinate transformation is represented by a homogeneous transformation matrix, wherein the transformation matrix T= [ R|t;0|1], wherein R is a 3×3 rotation matrix, and T is a 3×1 translation vector. The calculation precision of the rotation matrix is set to be 1/10 of the precision level, the precision of the rotation angle is 0.1 degree in the high precision mode, and the precision of the corresponding rotation matrix element is 0.002. The accuracy of the translation vector is set to 1/5 of the position accuracy, and the translation accuracy in the high-accuracy mode is 0.2cm. The numerical stability of the transformation parameters is controlled by a condition number, and the matrix is regularized when the condition number is greater than 100. The parameter update frequency matches the frequency of change of the force distribution topology, with a typical update frequency of 10-100Hz. The storage of the transformation matrix adopts a double-precision floating point format, so that the precision of numerical calculation is ensured. The inverse transform matrix is obtained by matrix inversion computation, T ^-1 = [ RT| -RT x T;0|1], where RT is the transpose of the rotation matrix. The accumulated error of the transformation chain is controlled by periodically resetting the reference coordinate system, and the reset period is set according to the precision requirement.

And carrying out space transformation on the foot end force distribution topology by utilizing the coordinate transformation parameters to establish a dynamic coordinate anchor point. And applying the set coordinate transformation parameters to the foot end force distribution topology, and establishing dynamic coordinate anchor points taking topological characteristics as references through space transformation operation. The transformation operation includes two steps of rotation transformation and translation transformation, first rotation alignment and then position correction. The transformed foot end position R' i=r×ri, where ri is the original foot end position and R is the rotation matrix. The translated foot end position r″ i=r' i+t, where t is the translation vector. The direction of the transformed force vector also needs to be adjusted accordingly, and the transformed force vector F' i=r×fi keeps the physical meaning of the force unchanged. The spatial position of the anchor point is determined by the transformed topological geometrical center, anchor point position oa=Σ (F 'i×r″ i)/Σ (F' i). The three axial directions of the anchor point coordinate system are determined through principal component analysis, the first principal component direction is an x-axis, the second principal component direction is a y-axis, and the z-axis is determined through a right-hand rule. The origin of the coordinate system is arranged at the anchor point position to form a complete dynamic coordinate anchor point. After the anchor point is established, all subsequent environment sensing and reconstruction operations are performed by taking the anchor point coordinate system as a reference.

The multi-frequency probe beam is transmitted from the dynamic coordinate anchor point to the ambient space. The detection beam is realized by adopting an ultrasonic technology, the transmitter is arranged in the front, back, left and right directions of the robot body, and 3-5 transmitting units with different frequencies are configured in each direction. The transmitting frequency range fr=20-100 kHz comprises three frequency bands of low frequency band (20-40 kHz), medium frequency band (40-70 kHz) and high frequency band (70-100 kHz), and different frequency bands have different detection characteristics. The low-frequency beam has strong penetrating capability, is suitable for long-distance coarse detection, the detection range is 5-20 m, the medium-frequency beam has moderate resolution, is suitable for medium-distance fine detection, the detection range is 2-10 m, and the high-frequency beam has high resolution, is suitable for short-distance high-precision detection, and the detection range is 0.5-5 m. The beam emission mode adopts fan-shaped radiation, the opening angle of a single beam is 15-30 degrees, and 360-degree omnibearing coverage is realized through multi-beam combination. The transmit power is adjusted according to the probe distance requirement, with p=p0× (r/r 0) ², where P0 is the reference power, r is the target probe distance, and r0 is the reference distance. The space directivity of the wave beam is realized by a phased array technology, and the wave beam pointing angle is changed by adjusting the phase difference of each transmitting unit. The transmitting sequence adopts a time division multiplexing mode, and beams with different frequencies are sequentially transmitted according to a preset time sequence, so that frequency interference is avoided.

In some embodiments, the coherent superposition of the multi-frequency detection beams to generate a hologram comprises generating a positive phase beam and a negative phase beam based on the multi-frequency detection beam by beam polarity identification, performing phase neutralization processing on the negative phase beam by using the positive phase beam to form a balanced beam, performing spatial aggregation superposition on the balanced beam to generate an aggregate wave field, and performing intensity curing on the aggregate wave field to generate the hologram.

The beam polarity identification based on the multi-frequency probe beam produces a positive phase beam and a negative phase beam. The reference for polarity recognition is the initial phase of the transmitted wave, so that the phase of the transmitted moment is a zero reference point. Phase detection phi = arctan (Im (E)/Re (E)), where E is an echo signal in complex form, im and Re representing the imaginary and real parts, respectively. Beams with a phase range between-pi and pi are classified as positive phase beams and beams with a phase range between pi and 2 pi are classified as negative phase beams. The positive phase beam corresponds to a reflected component in the same direction as the transmitted wave, typically from specular reflection from a smooth surface. The negative phase beam corresponds to the reflected component opposite the transmitted wave, typically from scattered reflection from a rough surface. The accuracy of the polarity recognition is determined by the phase measurement accuracy, which is typically 0.1 radians. The polarity recognition of the multi-frequency beams is performed in parallel, and each frequency component independently analyzes the phase characteristics thereof. The confidence of the identification result is comprehensively judged through the signal intensity and the phase stability, and the signal confidence of high intensity and stable phase is high. The spatial characteristics of the polar distribution reflect the material properties of the environmental surface, the metal surface mainly generating positive phase beams and the fabric surface mainly generating negative phase beams.

The method for forming the balanced beam by utilizing the positive phase beam to carry out phase neutralization treatment on the negative phase beam comprises the steps of taking the positive phase beam as a neutralization carrier to inject a neutralization signal into the negative phase beam, carrying out phase modulation on the negative phase beam based on the neutralization signal to generate a modulation beam, continuously correcting the phase deviation of the modulation beam by utilizing the positive phase beam, and curing the modulation beam to form the balanced beam after the phase deviation is eliminated.

And injecting a neutralizing signal into the negative phase beam by taking the positive phase beam as a neutralizing carrier. And injecting a phase correction signal into the negative phase beam by using the stable phase characteristic of the positive phase beam as a reference to realize the neutralization effect. The selection of the neutralizing carrier is based on the signal quality of the positive phase beam, and the positive phase beam with the highest signal-to-noise ratio is selected as the main carrier. The neutralization signal sc=a0×exp (i× (Φ+ - Φ -)), where A0 is the injection intensity, Φ+ is the positive phase beam phase, and Φ -is the negative phase beam phase. The injection intensity is determined according to the power ratio of the two types of beams, and the injection intensity a0=β× (P-/p+), where p+ and P-are the powers of the positive and negative phase beams, respectively, and β is the injection coefficient (typical value 0.5-1.0). The injection mode adopts a coherent mixing technology to coherently synthesize the neutralization signal and the negative phase wave beam. The time constant of the injection process is set to 1/10 of the beam coherence time, ensuring that the injection process does not destroy the beam coherence. And the neutralization of a plurality of negative phase beams adopts a time-sharing injection mode, so that mutual interference is avoided.

Phase modulating the negative phase beam based on the neutralization signal produces a modulated beam. And modulating the phase of the negative phase beam in real time by using the injected neutralization signal, so that the phase characteristics of the negative phase beam are closed to the positive phase beam. The basic principle of phase modulation is to achieve phase correction by varying the instantaneous frequency of the beam. Post-modulation phase phim=phij+Δphixsin (ωm×t), where Δphij is the modulation depth, ωm is the modulation frequency, and t is time. The modulation depth is determined from the initial phase difference, modulation depth Δφ= phi + -phi-/2, the modulation frequency is set to 1/100 of the carrier frequency. The modulating waveform adopts a sine waveform, so that the smoothness of the modulating process is ensured. Nonlinear effects in the modulation process are compensated by a predistortion technology, so that linearity of a modulation result is ensured. The multi-level modulation structure adopts two-level modulation of coarse modulation and fine modulation, the coarse modulation is close to the target phase rapidly, and the fine modulation realizes the precise alignment. The spectral characteristics of the modulated beam are monitored by spectral analysis, avoiding unnecessary frequency components introduced during the modulation process.

The phase deviation of the modulated beam is continuously corrected by the positive phase beam. The phase deviation is used as a feedback signal to control the modulation parameter by adopting a feedback control principle. Phase deviation e (t) =φ+ (t) - φm (t), where φ+ (t) is the instantaneous phase of the positive phase beam and φm (t) is the instantaneous phase of the modulated beam. The correction signal is generated by a proportional-integral controller, the correction signal u (t) =kp×e (t) +ki×ζ e (t) dt, where Kp is the proportional gain and Ki is the integral gain. The setting of the controller parameters is based on the dynamics of the system, typically kp=0.5, ki=0.1. The correction frequency is set to 10 times of the modulation frequency, and the real-time performance of correction is ensured. The stability of the continuous correction is ensured by a phase locking ring technology, and the locking range of the locking ring is + -pi/4. The convergence of the correction process is analyzed through the Liapunov stability theory, so that the system is ensured to stably converge to a target state. The multi-beam correction adopts an independent control mode, and the correction process of each beam is carried out independently.

Curing the modulated beam to form a balanced beam when the phase offset is eliminated. And stopping the modulation and correction process when the phase deviation is reduced below a preset threshold value, and solidifying the current modulation beam into a final balance beam. The curing criterion is that the root mean square value of the phase deviation is less than 0.05 radians and the duration exceeds 10 modulation cycles. Balance beam eb=am×exp (i×Φb), where Am is the amplitude after curing and Φb is the phase after curing. The curing process comprises two steps of parameter locking and state saving, wherein the parameter locking prevents parameter drift after curing, and the state saving records all beam parameters at the curing time. The amplitude is solidified by adopting a power averaging method, and the geometric average value of the positive and negative phase beam power is taken by solidifying amplitude Am= v ((P++ P-)/2). The solidification of the phase adopts a weighted average method, and the weight is determined according to the signal quality of each wave beam. The beam stability after curing is verified by long-term monitoring, and the stability time exceeds 100 wavelength periods to be considered as successful in curing.

The balanced beams are spatially aggregated and superimposed to produce an aggregated wave field. And aggregating the balanced beams subjected to the phase neutralization treatment according to the spatial positions to form an aggregated wave field covering the whole detection area. The basis of the aggregation operation is to perform superposition synthesis on the space coordinates of balanced beams with different directions and different frequencies. Aggregate wavefield F (x, y, z) =Σ (Eb, i (x, y, z) ×wi), where Eb, i is the complex amplitude of the ith balanced beam at spatial point (x, y, z), wi is a weight factor. The weight factors are determined according to the signal quality of the beams, the weight of the beams with high signal to noise ratio is large, and the weight of the beams with low signal to noise ratio is small. The spatial interpolation method employs tri-linear interpolation to interpolate discrete beam data onto a continuous spatial grid. The spatial resolution of the aggregate is determined by the wavelength of the highest frequency beam, with a typical spatial sampling interval of lambda/4. The range of the aggregate area is determined by the coverage of all beams, typically a spherical area with a radius of 5-20 meters.

Intensity curing the polymerized wave field to produce a hologram. The amplitude and phase information in the aggregated wave field is converted into a hologram data format that can be stored and processed. The process of intensity curing is a process of discretizing a continuous wave field distribution into a digital image. Hologram intensity I (u, v) = |f (x, y, z) | ², where (u, v) is the pixel coordinates of the hologram and (x, y, z) is the corresponding spatial coordinates. The phase information is encoded using a complex representation, and the amplitude and phase are combined into a complex format for storage. Hologram complex number H (u, v) =i (u, v) ×exp (i×Φ (u, v)), where Φ (u, v) is the phase distribution. The digital precision is set to 8-16 bits, and the proper quantization precision is selected according to the application requirement. The size of the hologram is determined by the size of the detection area and the required resolution, with typical dimensions of 512 x 512 to 2048 x 2048 pixels. The solidification process comprises the preprocessing steps of amplitude normalization, phase expansion, data compression and the like. The normalization operation maps the amplitude value to the range of 0-255, facilitating image display and processing. The phase unwrapping eliminates phase jumps of 2 pi, resulting in a continuous phase distribution.

The hologram is used to reconstruct the geometric topology of the environment. And restoring the interference pattern in the hologram into the spatial distribution of the object by adopting a holographic reconstruction algorithm. Reconstructing the object distribution O (x, y, z) =fft ^-1 [ H (u, v) ×r (u, v) ], where H (u, v) is the frequency domain representation of the hologram, R (u, v) is the reconstructed reference wave, and FFT ^-1 represents the inverse fourier transform. The identification of the object surface is achieved by analyzing the intensity distribution in the reconstruction result, the intensity peaks corresponding to the object surface positions. The extraction of the geometric topology comprises the steps of surface normal vector calculation, boundary recognition, connectivity analysis and the like. The surface normal vector n= ∇ O (x, y, z)/| ∇ O (x, y, z) |, where ∇ represents the gradient operator. The representation of the topology adopts a triangular mesh model to discretize the object surface into a collection of triangular patches. The accuracy of the grid is determined by the resolution of the hologram, and a high resolution hologram can generate a fine grid model. Global features of the topology include macroscopic information of the number of objects, volume, surface area, shape class, etc. The local features include detailed information such as surface roughness, curvature, normal vector distribution, etc. The fusion processing of the multi-frame holograms can improve reconstruction accuracy, and the defect of single-frame information is compensated through time sequence information. Dynamic features of the environment topology are detected through continuous reconstruction, and moving objects and static backgrounds in the environment are identified. The coordinate system of the reconstruction result is kept consistent with the dynamic anchor point coordinate system, and the space reference accuracy of the geometric information is ensured.

And step S140, performing energy density analysis in the environment geometric topology to identify a high-impedance area and a low-impedance area, performing potential field detouring on the high-impedance area to form a potential energy track, performing streamline tracking on the low-impedance area to form an energy flow track, and constructing an energy optimal path based on the potential energy track and the energy flow track.

Specifically, energy density analysis is performed in an ambient geometry topology to identify high impedance regions and low impedance regions. The basis of energy density analysis is to convert geometric information in the environment topology into energy impedance information and establish a mapping relation between the space position and the energy consumption. Impedance density ρ (x, y, z) =k×h (x, y, z) +α×s (x, y, z) +β×m (x, y, z), where h is a height gradient, s is surface roughness, m is material hardness, and k, α, β are weight coefficients. The height gradient is obtained by calculating the normal vector change rate of the topological surface, namely the change amplitude of the algorithm vector in space is calculated, and the region with large change amplitude corresponds to the steep-gradient terrain. . The surface roughness is calculated by analyzing the high-frequency change characteristics of the surface geometry, and the surface with large roughness corresponds to a high-energy consumption area. The material hardness information is deduced from the reflection intensity of the hologram, and the reflection intensity of hard materials such as metal is large and the reflection intensity of soft materials is small. The classification of the impedance areas is based on the statistical distribution of the impedance density, the high impedance areas are defined as areas where the impedance density is greater than the mean plus 1 times the standard deviation, and the low impedance areas are defined as areas where the impedance density is less than the mean minus 1 times the standard deviation. The high impedance areas generally correspond to high energy consumption locations such as steep slopes, rough terrain, obstacle edges, etc. The low impedance region generally corresponds to a low energy consumption location of flat ground, smooth surface, open space, etc.

Potential field bypassing is performed on the high-impedance region to form potential energy tracks. The potential field bypass is to consider high impedance regions as potential barriers in the potential field, bypassing these regions by constructing repulsive potential field guiding tracks. Repulsive potential field Ur (x, y) =kr×Σ (1/di ²), where kr is the repulsive coefficient and di is the distance to the centroid of the ith high impedance region. The height of the barrier is proportional to the impedance density, and the higher the impedance the higher the barrier corresponding to the region. The range of influence of the potential field is determined according to the geometry of the high impedance region, with the influence radius ri=λ× (Ai/pi), where λ is the influence coefficient (typical value 2-3) and Ai is the area of the i-th region. The detour track is generated by a potential field gradient descent method, and the track direction v= - ∇ (ur+ug), wherein Ug is a guiding potential field, guiding the robot to move towards the target direction. The potential fields of the plurality of high-impedance areas are synthesized by the superposition principle to form complex potential field distribution. The detour strategy includes two modes, tangential detour, which moves along a equipotential line, and normal detour, which moves perpendicular to the equipotential line. The length of the potential energy track is calculated through the integral arc length, and the track length L= ≡v (t) ||dt, wherein the integral range is the whole detour process. The bypass energy cost comprises two parts of path extension cost and potential energy rising cost, and the total cost is calculated through weighted summation.

And carrying out streamline tracking on the low-impedance area to form an energy flow track. Streamline tracking is the dominant path of energy flow seen by the low impedance region, along which the trajectory is guided by the construction of the flow field. Energy flow field E (x, y) = - ∇ (1/ρ (x, y)), where ρ is the impedance density and the negative gradient direction is the energy flow direction. The streamline is generated by adopting a numerical integration method, and gradually advances along the flow field direction from the starting point. The flow line equation describes that the change rate of the position along with the parameters is equal to the normalized energy flow field direction, so that the flow line is ensured to be propelled along the unit direction of energy flow. The integral step length is adaptively adjusted according to the change rate of the flow field, a small step length is adopted in a region with severe change, and a large step length is adopted in a region with gentle change. The convergence of the streamline is analyzed by the divergence of the flow field, and the regional streamline with negative divergence is converged and the regional streamline with positive divergence is diverged. The convergence and bifurcation points of the multiple streamlines constitute key nodes of the streamline network, which typically correspond to the junction locations of the energy transfer. The density distribution of the streamline reflects the intensity of energy flow, and the energy flow in the area with high density is strong and the energy flow in the area with low density is weak. The energy flow track is selected based on the energy transmission efficiency of the streamline, wherein the transmission efficiency eta= [ ethylene ] [ dl/[ ethylene ] [ dl ], and dl is track infinitesimal. The curvature control of the track ensures that the robot can move smoothly along the track, and the radius of curvature is not less than the minimum turning radius of the robot.

In some embodiments, the construction of the energy optimal path based on the potential energy track and the energy flow track comprises the steps of carrying out energy scanning on the potential energy track and the energy flow track to identify energy distribution hot spots, locating a lowest energy consumption interval based on the energy distribution hot spots, carrying out path locking on the lowest energy consumption interval to form a candidate path section, and carrying out connection assembly on the candidate path section to construct the energy optimal path.

And carrying out energy scanning on the potential energy track and the energy flow track to identify energy distribution hot spots. And setting a scanning window with a fixed size on the track by adopting a moving window technology, and calculating the average energy density in the window point by point. Scanning window energy density Ed (i) = (1/N) ×Σ (ρ (xi)), where N is the number of sample points within the window and xi is the i-th sample point location. The size of the scanning window is set to be 1.5 times of the length of the robot body, so that the scanning result is ensured to be matched with the motion characteristic of the robot. The threshold for hot spot identification is set to 1.5 times the average energy density of the track, and the areas exceeding the threshold are marked as energy hot spots. The threshold for cold spot identification is set at 0.5 times the average energy density of the track, and areas below the threshold are marked as energy cold spots. The hot spot areas generally correspond to high energy consumption segments in the track, such as tight turns, climbs, detours, etc. movement patterns. The cold spot area generally corresponds to a low energy segment of the trajectory, such as a straight travel, downhill, downwind, etc. motion pattern.

And positioning the lowest energy consumption interval based on the energy distribution hot spot. And positioning a continuous section with the lowest energy consumption in the track by using the identified energy hot spot and cold spot distribution information through a section analysis method. The positioning of the lowest energy consumption interval is based on a continuity principle, and a continuous low-energy density area is searched to form a complete path segment. The interval energy consumption evaluation function J (a, b) = ≡ [ a, b ] ρ(s) ds, where [ a, b ] is the interval end point and s is the track arc length parameter. The constraint of the section length ensures that each section has a sufficient path length, the minimum section length being set to 3 times the robot body length. The continuity test of the interval is considered to be continuous and stable by analyzing the change rate of the energy density in the interval, and the interval with the change rate smaller than the threshold value. The comparison of the candidate intervals adopts a normalized energy consumption index, the normalized energy consumption Ne=J (a, b)/(b-a), and the interval energy consumption efficiency with small values is high. The accurate positioning of the interval boundary is realized by energy gradient analysis, and the position with the minimum gradient change is taken as the interval boundary. The screening of the local optimal interval takes multiple factors such as interval length, energy consumption level, continuity and the like into consideration, and adopts a comprehensive grading method for sorting.

And performing path locking on the lowest energy consumption interval to form a candidate path segment. The path locking process comprises the steps of track parameterization, end point calibration, middle point interpolation and the like. Trajectory parameterization uses cubic spline curve fitting, path function P (t) =Σ (ai×t ³+bi×t² +ci×t+di), where t is a parameter and ai, bi, ci, di is a spline coefficient. The end point calibration ensures that the starting and ending point positions of the path segments are accurate, and the end point coordinates are fitted by a least square method. The interpolation of the middle points ensures that the internal shape of the path section is smooth, and the interpolation density is adaptively adjusted according to curvature change. The locked path segment has a fixed geometry and parametric representation and is no longer affected by the original trajectory variations. The number of the candidate path segment adopts a hierarchical naming rule and comprises information such as track type, interval sequence number, segment sequence number and the like. The quality evaluation of the path segment is comprehensively measured by geometric indexes and energy indexes, wherein the geometric indexes comprise length, curvature and smoothness, and the energy indexes comprise average energy consumption, energy consumption variance and efficiency grade. The ranking of the plurality of candidate path segments is based on the composite quality score, with path segments with high scores being preferentially used for path splicing.

And performing connection assembly on the candidate path segments to construct an energy optimal path. The basic strategy of connection assembly is to select path segments according to the energy efficiency priority principle, and ensure the geometric continuity of the paths. The assembly cost function c=Σ (ei×li) +Σ (pj×lj), where Ei is path segment energy consumption, li is path segment length, pj is connection cost, and Lj is connection length. The selection of the path segments adopts a dynamic programming method, and the optimal candidate path segments are selected segment by segment from the starting point. The connection strategy comprises three modes of direct connection, arc connection and spline connection, and a proper connection mode is selected according to the geometric characteristics of the connection points. Direct connection is suitable for the condition that the end points are closer and in the same direction, and the connection cost is minimum but geometric discontinuity is possible. The arc connection is suitable for the situation that the direction needs to be changed, and smooth transition is realized through the arc section. Spline connection is suitable for complex geometric connections, providing the highest continuity but being computationally expensive. The quality of the connection is evaluated by means of the indexes such as curvature continuity, angle change rate, length increment and the like. The geometric features of the global path include parameters such as total length, average curvature, maximum rotation angle, smoothness index, etc.

And S150, constructing an oscillation analysis space based on the gravity center disturbance mode, projecting an energy optimal path to the oscillation analysis space for simulation to generate a gravity center resonance mode, identifying resonance risk points from the gravity center resonance mode, and determining a stabilization parameter by using the resonance risk points.

Specifically, an oscillation analysis space is constructed based on the barycentric disturbance mode. The construction of the oscillation analysis space comprises three steps of spatial domain expansion, frequency domain transformation and phase space mapping. The spatial domain expansion expands the two-dimensional gravity center disturbance mode into a three-dimensional oscillation space, the oscillation space V (x, y, ω) =fft [ D (x, y, t) ], where ω is the frequency dimension, and the FFT represents the fast fourier transform. The frequency domain transformation converts the disturbance information of the time domain into the oscillation characteristics of the frequency domain, so that the oscillation behaviors of different frequency components can be analyzed conveniently. The construction of the phase space is based on gradient information of the disturbance modes, and the propagation direction characteristics are determined by calculating partial derivatives of the disturbance in the x and y directions. The resolution setting of the oscillation analysis space is consistent with the original disturbance mode, the spatial resolution is 2cm multiplied by 2cm, and the frequency resolution is 0.1Hz. The boundary condition of the space is set as a periodic boundary, and the continuity of oscillation analysis is ensured. The superposition of the multiple frequency layers forms a three-dimensional oscillation analysis space, and each frequency layer corresponds to the oscillation characteristic of a specific frequency component. The space coordinate system is consistent with the dynamic anchor point coordinate system, so that the space reference accuracy of the analysis result is ensured. The oscillation intensities unify the oscillation amplitudes of different frequency components to the same magnitude through normalization processing. The space is stored in a three-dimensional matrix format, and efficient space inquiry and interpolation operation are supported.

And projecting the energy optimal path into an oscillation analysis space to simulate to generate a gravity center resonance mode. The basic principle of path projection is to calculate the response of each point in the path in the oscillation analysis space by using the point as an oscillation excitation source. Path projection is achieved by mapping path positions to oscillating spatial coordinates, taking into account the geometric features and motion characteristics of the path. The simulation process adopts a finite element method to discretize the oscillation analysis space into grid cells, and an oscillation equation is solved in each cell. Oscillation equation ∂ ²φ/∂t²+2γ∂φ/∂t+ω₀ ² phi = F(s), where phi is oscillation amplitude, gamma is damping coefficient, omega ₀ is natural frequency, and F(s) is path excitation. In the solving process of the oscillation equation, disturbance propagation direction information provided by the phase space coordinates is considered, so that oscillation propagation is ensured to accord with a physical rule. The determination of the natural frequency is calculated based on the dynamic parameters of the robot through the ratio of the equivalent stiffness to the equivalent mass. The damping coefficient is set according to the structural damping characteristic of the robot, and the typical value is 0.05-0.2. The excitation intensity on the path is related to the curvature and speed of the path, with path segments with large curvature and high speed producing stronger excitation. The generation of the resonant mode superimposes the oscillating responses of all points on the path by the principle of superposition. Resonant mode R (x, y, t) =Σ [ phi i (t) ×g (x-xi, y-yi) ], where phi is the oscillating response of the ith path point and G is the green's function. The green function describes the propagation characteristics of point excitations in space, in the form of a gaussian distribution. The simulated time step is set to 1/100 of the oscillation period, ensuring the stability of the numerical calculation. The spectral characteristics of the resonant modes are obtained by frequency domain analysis, identifying the principal resonant frequency components.

In some embodiments, the identifying resonance risk points from the barycentric resonance modes includes constructing a resonance magnitude map based on the barycentric resonance modes, searching for peak mutation locations in the resonance magnitude map, performing stability assessment on the peak mutation locations to generate risk levels, and marking resonance risk points based on the risk levels.

A resonance amplitude profile is constructed based on the barycentric resonance modes. The oscillation amplitude information of the gravity center resonance mode R (x, y, t) is organized into a two-dimensional map according to the spatial position and the frequency component, so that visual analysis and processing are facilitated. The first step in the construction of the atlas is to extract the amplitude envelope of the resonant modes and obtain the maximum oscillation amplitude of each spatial position. In the map construction process, the spatial propagation characteristics of the Grignard function are considered, so that the amplitude distribution is ensured to reflect the real spatial coupling relation. The horizontal axis of the map represents the spatial position coordinate, and can be one-dimensional path arc length coordinate or two-dimensional Cartesian coordinate. The vertical axis represents the magnitude of the oscillation amplitude, with a logarithmic scale to show large dynamic range amplitude variations. The resolution of the spectrum is consistent with the original resonant mode, ensuring that important detailed information is not lost. The patterns of the multiple frequency components are respectively constructed by frequency decomposition technology, and each frequency component corresponds to an independent pattern. The color coding of the map adopts a thermodynamic diagram mode, red represents a high-amplitude region, and blue represents a low-amplitude region, so that the abnormal region can be identified quickly.

The resonance amplitude profile is searched for peak mutation locations. And automatically searching positions with abnormally increased amplitude in the constructed resonance amplitude map by an image processing technology, wherein the positions correspond to potential resonance phenomena. The basic method of peak searching is to find local maximum points, i.e. locations where the magnitude value is larger than all surrounding neighboring points. The setting of the search threshold is based on the statistical properties of the spectrum, and the peak identification threshold is determined by calculating the amplitude mean and standard deviation. The mutation detection is realized by calculating the magnitude of the amplitude gradient, and the position with large gradient corresponds to the amplitude mutation. Edge detection algorithms are used to identify sharp boundaries in the amplitude spectra, which generally correspond to the edges of the resonance region. The morphological analysis of the peak distinguishes two types of peaks and a platform, the peaks correspond to local resonance, and the platform corresponds to regional resonance. The multi-scale search adopts search windows with different sizes, wherein small windows detect local peaks and large windows detect global peaks.

Stability assessment of peak mutation locations yields risk ratings. The stability evaluation is carried out from four dimensions, namely the ratio relation of the amplitude risk evaluation peak value to the safety threshold value, the proximity degree of the frequency risk evaluation peak value corresponding to the frequency and the natural frequency of the system, the influence of the space risk evaluation peak value position on a critical path, and the duration time and the reproduction frequency of the time risk evaluation peak value. The peak value mutation with large gradient has higher risk by combining amplitude gradient information in the evaluation process. The comprehensive risk index is calculated through weighted combination, and the comprehensive risk index R=w1×Da+w2×Df+w3×Ds+w4×Dt, wherein Da, df, ds, dt is the risk degree of four dimensions respectively, and the weight coefficient is determined according to the system characteristics. The risk level is divided into a numerical range based on the comprehensive risk index, namely, high risk (R > 0.8), medium risk (R <0.4 > 0.8) and low risk (R < 0.4).

Resonance risk points are marked based on risk levels. And the risk point code comprises information such as risk level, region number, serial number and the like by adopting a layering coding mode. The high risk points are marked with red marks, highlighted by red circles in the map, and marked with risk index values. The medium risk points are marked by yellow, and are marked by yellow triangles. The low risk points are marked with green marks, indicated by green squares, and only the position information is recorded. The attribute information of the risk points comprises the fields of position coordinates, risk grades, risk indexes, main risk factors, influence ranges, processing suggestions and the like. The spatial distribution analysis of the marking results identifies the aggregation mode of the risk points, and the aggregation area needs to be focused on. Hierarchical management of risk points establishes processing priority and response time requirements for risk points of different levels.

The resonance risk points are used to determine the stabilization parameters. The design of the stabilization parameters is based on the active damping principle, and the damping characteristics of the system are changed by adjusting the control parameters of the robot. Stabilized damping coefficient γs=γ ₀ +Δγ× Is, where γ ₀ Is the base damping coefficient, Δγ Is the adjustment increment, and Is the risk intensity. The spatial distribution of the damping coefficients is locally adjusted according to the positions of the risk points, the damping coefficients near the risk points are increased, and the areas far away from the risk points keep original damping. The frequency dependent stabilization parameters design different control strategies for different frequency components, and frequency selective control is realized through a frequency dependent gain function. The phase compensation parameter is used for adjusting the phase margin of the control system, and a mode of base phase time-varying compensation is adopted. The stabilizing parameters of the multi-input multi-output system are expressed in a matrix form, and a weight matrix is determined according to the influence degree of the risk points. The constraint condition of the stabilization parameters ensures that the stability of the system is not destroyed, and the parameter variation range is limited in a safe interval.

And step S160, carrying out quantum entanglement type synchronization on the dynamic coordinate anchor points based on the stabilization parameters to generate coordinate references, and carrying out fusion calibration on the coordinate references and the environment geometric topology to generate positioning coordinates.

In some embodiments, the quantum entanglement type synchronization of the dynamic coordinate anchor points based on the stabilization parameters to generate a coordinate reference comprises extracting phase characteristics and frequency characteristics based on the stabilization parameters, establishing a phase locking loop in the dynamic coordinate anchor points based on the phase characteristics, utilizing the frequency characteristics to perform frequency calibration on the phase locking loop to generate a synchronous signal, and generating the coordinate reference based on the synchronous signal.

The phase and frequency characteristics are extracted based on the stabilization parameters. The stabilization parameters contain a number of components and require efficient feature information extraction by signal decomposition techniques. The phase feature extraction adopts a hilbert transformation method, and the phase feature phi (t) =arg [ H (gamma _S (t)) ] is gamma _S (t) which is a stabilizing parameter of a time domain, wherein H represents the hilbert transformation, and arg represents a complex phase angle. The frequency characteristic is obtained by time derivative of the phase, reflecting the frequency variation characteristic of the stabilization parameter. The filtering pretreatment of the feature extraction adopts a band-pass filter to filter direct current components and high-frequency noise, and the useful frequency range is reserved. The unwrapping process of the phase characteristics eliminates phase jumps of 2pi, ensuring continuity of the phase sequence. The smoothing of the frequency characteristics uses moving average filtering, and the window length is set to 1/10 of the characteristic period. The multi-parameter feature fusion synthesizes the features of different stabilization parameters, and the weight is determined according to the importance of the parameters. The statistical characteristic analysis of the features comprises indexes such as mean, variance, correlation and the like. Periodic detection of the phase signature identifies the main periodic component of the signature signal, and the periodic information is used in the loop design.

A phase locked loop is established in the dynamic coordinate anchor based on the phase characteristics. And a close association synchronization mechanism similar to quantum entanglement is adopted, and a plurality of dynamic coordinate anchors realize instantaneous response and highly associated synchronization states through phase locking. And taking the extracted phase characteristic phi (t) as a reference phase, and establishing a phase locking loop in a plurality of dynamic coordinate anchor points to realize synchronization. The basic composition of the phase locking loop comprises a phase detector, a loop filter and a voltage-controlled oscillator. The phase detector calculates the difference between the phase of each anchor point and the reference phase, and the phase error is equal to the difference between the reference phase and the phase of each anchor point. The loop filter filters and amplifies the phase error, the filter output vi = K _P ×ei + ki×c eidt, where K _P is the proportional gain, ki is the integral gain, and ei is the phase error. The voltage-controlled oscillator adjusts the oscillation frequency of the anchor point according to the filter output. The multi-anchor loop adopts a full interconnection structure, and the state change of any anchor point can instantaneously influence all other anchor points to form non-local association characteristics similar to quantum entanglement. The design of the loop parameters is based on the locking range and locking time requirements. The stability of the loop is analyzed by transfer function to ensure the stability of the closed loop system. The judgment of the locking state is based on the standard deviation of the phase error, and the standard deviation is smaller than a set threshold value to realize locking. The dynamic response characteristics of the loop include indicators such as lock time, overshoot, steady state error, etc.

Frequency calibration of the phase locked loop using the frequency signature produces a synchronization signal. Based on the extracted frequency characteristic f (t) as a frequency reference, the established phase locking loop is subjected to accurate frequency calibration, so that the frequency precision and stability of the synchronous signal are ensured. The frequency calibration process maintains the tight coupling characteristics between multiple anchors, the frequency adjustment of each anchor has instantaneous relevance, and the calibration of one anchor can immediately affect the frequency state of the whole anchor network. The basic principle of frequency calibration is to use the frequency characteristic as a frequency reference and adjust the center frequency of the loop to be consistent with the frequency characteristic. The frequency calibration error is calculated by the difference between the reference frequency and the actual frequency of each loop. The calibration controller adopts a proportional-integral structure, and the control output is used for adjusting loop parameters. The numerically controlled oscillator adjusts the center frequency of the loop according to the calibration control output. The convergence of the calibration process is monitored by the trend of the frequency error, the decreasing error being indicative of the calibration convergence. The calibration of the multi-frequency components adopts a frequency division calibration technology, and the broadband frequency characteristic is decomposed into a plurality of narrowband components to be calibrated respectively. The calibration accuracy includes parameters such as frequency deviation, frequency stability, phase noise, etc. The generation of the synchronization signal is based on the calibrated loop output, synchronization signal S (t) =a×cos (2pi f 'i×t+phi), where a is the signal amplitude, f' i is the calibrated frequency, phi is the phase.

A coordinate reference is generated based on the synchronization signal. And generating a unified coordinate reference system by using the synchronous signals after frequency calibration as a time reference through a coordinate transformation algorithm. The generation of the coordinate reference reflects the inseparable characteristic of multiple anchor points, the coordinate information contributed by each anchor point forms a unified and inseparable reference system, and the change of any single anchor point can influence the overall reference state. The generation of the coordinate reference is based on the phase relationship of the synchronization signals, the reference phase being determined by a weighted combination of the synchronization phases of the anchor points. The origin of the reference coordinate system is determined by a weighted average of all anchor positions. The coordinate axis direction is determined based on a principal component analysis method, the first principal component is an x-axis, the second principal component is a y-axis, and the z-axis is determined by a right-hand rule. The time evolution of the reference coordinate system is obtained by phase integration of the synchronization signal. The coordinate transformation matrix describes the transformation relation from each anchor coordinate system to the reference coordinate system, and comprises a rotation matrix and a translation vector. The accuracy of the reference coordinates is determined by the phase noise of the synchronization signal. And carrying out statistical fusion on the reference coordinates generated by different anchor points by fusion processing of the multi-reference coordinates, and determining fusion weights according to the reliability of each anchor point. The stability of the reference coordinates is verified through long-term monitoring, and the reference is considered stable when the position drift is smaller than the set threshold value. The calibration period of the coordinate reference is determined according to the application precision requirement, the high-precision application needs frequent calibration, and the general application can prolong the calibration period. The reference information is stored to include the origin of coordinates, the axial direction, transformation parameters, accuracy indexes, and the like.

And performing fusion calibration based on the coordinate reference and the environment geometric topology to generate positioning coordinates. And performing space calibration and error correction on the coordinate reference by using the geometric characteristics of the environment. Extraction of geometric feature points stable geometric markers such as wall corners, pillars, door frames and other rigid structures are selected from the environment topology to serve as calibration references. The feature point weight is determined according to the stability and the distance, and the feature point weight with high stability and close distance is large. The matching of the coordinate reference and the geometric feature adopts a nearest neighbor searching algorithm to find the position closest to the geometric feature point in the reference coordinate system. The calculation of the match error is measured by euclidean distance. The calibration transformation uses a rigid body transformation model, which includes two components, rotation and translation. The transformation parameters are solved by a least square method, and the total matching error is minimized on all matching point pairs. Objective function j=Σwi×ei ², where wi is the feature point weight, ei is the matching error, and the optimal transformation parameters are solved by gradient descent method. The distribution of the fusion weight is determined according to the reliability of the coordinate reference and the geometric topology, and the data source with high reliability has large weight. The generation of the positioning coordinates is performed by weighted fusion calculation, and the positioning coordinates X _l = α×xβ+ (1- α) X Xe, where α is the fusion weight, xβ is the reference coordinate, and Xe is the geometric coordinate. The finally generated positioning coordinates integrate quantum entanglement type synchronous time reference information and space constraint information of the environment geometric topology, and high-precision space positioning is realized. The coordinate reference system ensures accurate synchronization among multiple anchor points through the phase locking loop, the frequency calibration ensures the stability of the time reference, and the geometric fusion calibration eliminates the system error. The whole positioning process builds a complete technical chain from gravity center disturbance analysis, path optimization and oscillation control to accurate positioning, and the four-legged robot has autonomous accurate positioning capability in a complex environment.

In order to execute the four-foot inspection robot space positioning method based on gravity sensing corresponding to the method embodiment, corresponding functions and technical effects are realized. Referring to fig. 2, fig. 2 shows a block diagram of a space positioning system 200 of a four-foot inspection robot based on gravity sensing according to an embodiment of the present application. For convenience of explanation, only the parts related to this embodiment are shown, and the four-foot inspection robot space positioning system 200 based on barycenter sensing provided in the embodiment of the present application includes:

the gravity sensing module 201 is configured to obtain a contact force and a body gesture angle of each foot of the four-foot robot, construct a gravity potential energy field based on the contact force, generate a fluctuation spectrum based on the body gesture angle, and couple the gravity potential energy field and the fluctuation spectrum to form a gravity disturbance mode;

The disturbance compensation module 202 is configured to parse a disturbance propagation path from the gravity center disturbance mode, perform reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence, and reconstruct a foot-end force distribution topology based on the anti-disturbance sequence;

The environment modeling module 203 is configured to establish a dynamic coordinate anchor point based on the foot end force distribution topology, transmit a multi-frequency probe beam from the dynamic coordinate anchor point to an environment space, perform coherent superposition on the multi-frequency probe beam to generate a hologram, and reconstruct an environment geometric topology by using the hologram;

The path planning module 204 is configured to perform energy density analysis in the geometric topology of the environment to identify a high-impedance region and a low-impedance region, perform potential field detouring on the high-impedance region to form a potential energy track, perform streamline tracking on the low-impedance region to form an energy flow track, and construct an energy optimal path based on the potential energy track and the energy flow track;

The resonance analysis module 205 is configured to construct an oscillation analysis space based on the barycentric disturbance mode, project the energy optimal path to the oscillation analysis space to simulate to generate a barycentric resonance mode, identify a resonance risk point from the barycentric resonance mode, and determine a stabilization parameter using the resonance risk point;

And the positioning generation module 206 is configured to perform quantum entanglement type synchronization on the dynamic coordinate anchor point based on the stabilization parameter to generate a coordinate reference, and perform fusion calibration on the coordinate reference and the environment geometric topology to generate a positioning coordinate.

The above-mentioned four-foot inspection robot space positioning system 200 based on the gravity center sensing may implement the four-foot inspection robot space positioning method based on the gravity center sensing in the above-mentioned method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the above method embodiments, and in this embodiment, no further description is given.

The foregoing embodiments are provided for the purpose of exemplary reproduction and deduction of the technical solution of the present invention, and are used for fully describing the technical solution, the purpose and the effects of the present invention, and are used for enabling the public to understand the disclosure of the present invention more thoroughly and comprehensively, and are not used for limiting the protection scope of the present invention.

The above examples are also not an exhaustive list based on the invention, and there may be a number of other embodiments not listed. Any substitutions and modifications made without departing from the spirit of the invention are within the scope of the invention.

Claims (10)

1. A spatial localization method for a quadruped inspection robot based on center of gravity perception, characterized in that it includes: The contact force and body posture angle of each foot of the quadruped robot are obtained. A center of gravity potential energy field is constructed based on the contact force. A wave spectrum is generated based on the body posture angle. The center of gravity potential energy field and the wave spectrum are coupled to form a center of gravity perturbation mode. The disturbance propagation path is analyzed from the center of gravity disturbance mode, the disturbance propagation path is inversely compensated to generate an anti-disturbance sequence, and the foot force distribution topology is reconstructed based on the anti-disturbance sequence; Based on the foot force distribution topology, a dynamic coordinate anchor point is established, and a multi-frequency detection beam is emitted from the dynamic coordinate anchor point to the environmental space. The multi-frequency detection beam is coherently superimposed to generate a hologram, and the environmental geometry topology is reconstructed using the hologram. Energy density analysis is performed in the environmental geometry topology to identify high-impedance and low-impedance regions. Potential field traversal is performed in the high-impedance region to form a potential energy trajectory, and streamline tracing is performed in the low-impedance region to form an energy flow trajectory. Based on the potential energy trajectory and the energy flow trajectory, an optimal energy path is constructed. An oscillation analysis space is constructed based on the aforementioned center of gravity perturbation mode. The optimal energy path is projected onto the oscillation analysis space to simulate and generate a center of gravity resonance mode. Resonance risk points are identified from the center of gravity resonance mode, and stabilization parameters are determined using the resonance risk points. Based on the stabilization parameters, the dynamic coordinate anchor points are quantum entangled to synchronously generate coordinate references, and the coordinate references are fused and calibrated with the environmental geometry to generate positioning coordinates. 2. The method according to claim 1, characterized in that, the step of coupling the center-of-gravity potential energy field with the wave spectrum to form a center-of-gravity perturbation mode includes: Locate the potential energy extremum point in the aforementioned center-of-gravity potential energy field; A coupling frequency is generated by frequency matching between the potential energy extreme point and the wave spectrum. The coupling frequency is used to excite the potential energy field to oscillate and generate a disturbance wave; A centroid perturbation mode is formed based on the perturbation wave. 3. The method according to claim 1, characterized in that, establishing dynamic coordinate anchor points based on the foot force distribution topology includes: The anchor point accuracy is determined based on the complexity of the foot force distribution topology assessment, whereby the complexity includes force distribution uniformity, topological stability, and spatial symmetry. Set the coordinate transformation parameters according to the anchor point accuracy; The coordinate transformation parameters are used to perform spatial transformation on the topology of the foot force distribution to establish dynamic coordinate anchor points. 4. The method according to claim 1, characterized in that, the step of coherently superimposing the multi-frequency detection beams to generate a hologram includes: Based on the multi-frequency detection beam, beam polarity identification is performed to generate positive-phase beams and negative-phase beams; The positive-phase beam is used to perform phase neutralization processing on the negative-phase beam to form a balanced beam. The balanced beams are spatially aggregated and superimposed to generate a converged wave field; The aggregated wave field is intensity-solidified to generate a hologram. 5. The method according to claim 1, wherein constructing the optimal energy path based on the potential energy trajectory and the energy flow trajectory comprises: Energy distribution hotspots are identified by energy scanning of the potential energy trajectory and the energy flow trajectory; The lowest energy consumption range is located based on the energy distribution hotspots; The lowest energy consumption range is used to generate candidate path segments by path locking. The candidate path segments are connected and assembled to construct the energy-optimal path. 6. The method according to claim 1, wherein identifying resonance risk points from the centroid resonance mode comprises: A resonance amplitude spectrum is constructed based on the described centroid resonance mode; Search for the peak abrupt location in the resonance amplitude spectrum; A stability assessment is performed on the peak mutation locations to generate a risk level; Resonance risk points are marked based on the aforementioned risk level. 7. The method according to claim 1, characterized in that, the step of generating a coordinate reference by quantum entanglement-based synchronization of the dynamic coordinate anchor point based on the stabilization parameter includes: Phase and frequency features are extracted based on the stabilization parameters; A phase-locked loop is established in the dynamic coordinate anchor point based on the phase characteristics; The frequency characteristics are used to perform frequency calibration on the phase-locked loop to generate a synchronization signal; A coordinate reference is generated based on the synchronization signal. 8. The method according to claim 2, characterized in that, the step of generating a disturbance wave by exciting the potential energy field oscillation using the coupling frequency comprises: The initial oscillation of the potential energy field is initiated based on the coupling frequency; The initial oscillation of the potential energy field is accelerated and amplified to form enhanced oscillation; Controlled oscillations are generated by amplitude control through the enhanced oscillations; The controlled oscillation is braked and stabilized to generate a disturbance wave. 9. The method according to claim 4, characterized in that, the step of using the positive phase beam to perform phase neutralization processing on the negative phase beam to form a balanced beam includes: The positive phase beam is used as a neutralization carrier to inject a neutralization signal into the negative phase beam; Based on the neutralization signal, the negative phase beam is phase-modulated to generate a modulated beam; The phase deviation of the modulation beam is continuously corrected using the positive phase beam; Once the phase deviation is eliminated, the modulated beam is solidified to form a balanced beam. 10. A spatial positioning system for a quadrupedal inspection robot based on center of gravity perception, characterized in that it comprises: The center of gravity sensing module is used to acquire the contact force at each foot of the quadruped robot and the body posture angle. Based on the contact force, a center of gravity potential energy field is constructed, and based on the body posture angle, a wave spectrum is generated. The center of gravity potential energy field and the wave spectrum are coupled to form a center of gravity perturbation mode. The disturbance compensation module is used to parse the disturbance propagation path from the center of gravity disturbance mode, perform reverse compensation on the disturbance propagation path to generate an anti-disturbance sequence, and reconstruct the foot force distribution topology based on the anti-disturbance sequence; The environmental modeling module is used to establish dynamic coordinate anchor points based on the foot force distribution topology, emit multi-frequency detection beams from the dynamic coordinate anchor points into the environmental space, coherently superimpose the multi-frequency detection beams to generate a hologram, and reconstruct the environmental geometry topology using the hologram; The path planning module is used to perform energy density analysis in the environmental geometry to identify high-impedance and low-impedance regions, perform potential field circumvention in the high-impedance region to form a potential energy trajectory, perform streamline tracing in the low-impedance region to form an energy flow trajectory, and construct an energy-optimal path based on the potential energy trajectory and the energy flow trajectory. The resonance analysis module is used to construct an oscillation analysis space based on the center of gravity disturbance mode, project the optimal energy path into the oscillation analysis space to simulate and generate a center of gravity resonance mode, identify resonance risk points from the center of gravity resonance mode, and use the resonance risk points to determine stabilization parameters. The positioning generation module is used to generate a coordinate reference by quantum entanglement synchronously based on the stabilization parameters of the dynamic coordinate anchor point, and to generate positioning coordinates by fusion calibration based on the coordinate reference and the environmental geometry and topology.