CN121140833B

CN121140833B - Automobile path planning method based on dynamic pheromone punishment coefficient and application

Info

Publication number: CN121140833B
Application number: CN202511695392.2A
Authority: CN
Inventors: 王骏骋; 王建富; 王奕超
Original assignee: Zhejiang Sci Tech University ZSTU
Current assignee: Zhejiang Sci Tech University ZSTU
Priority date: 2025-11-19
Filing date: 2025-11-19
Publication date: 2026-02-06
Anticipated expiration: 2045-11-19
Also published as: CN121140833A

Abstract

This scheme provides a vehicle path planning method and its application based on dynamic pheromone penalty coefficient. The navigation path is obtained by multiple iterative searches based on the optimized ant colony algorithm. The deadlock pheromone penalty, elite cooperative pheromone reward, and path optimization pheromone reward are introduced into the pheromone update formula of the ant colony algorithm to improve the path convergence stability and optimize the path quality, thereby improving the path planning ability of the ant colony optimization algorithm in complex environments.

Description

Automobile path planning method based on dynamic pheromone punishment coefficient and application

Technical Field

The application relates to the field of vehicle path planning and autonomous navigation, in particular to an automobile path planning method based on a dynamic pheromone punishment coefficient and application thereof.

Background

The global path planning is based on static prior information such as a high-precision map and the like, and calculates an optimal macroscopic driving route from a starting point to an end point for the vehicle. The existing global path planning method is more in variety, such as an A algorithm, a Dijkstra algorithm, a rapid random search tree (RRT), an optimization algorithm thereof and the like, wherein the A algorithm is used for searching an optimal path efficiently by combining a heuristic function, but the performance of the A algorithm is seriously dependent on the heuristic function design, the Dijkstra algorithm can ensure that the global shortest path can be found, but the calculation efficiency is lower in large-scale graph searching, and the RRT algorithm has probability completeness in a high-dimensional complex space, but the generated path is usually not optimal and has randomness. In summary, the above algorithm has difficulty in achieving efficient computation while guaranteeing path quality in a large-scale, high-dimensional or unstructured road surface environment.

The ant colony optimization algorithm is used as an effective heuristic search method, and can find an approximate optimal solution when solving the path planning problem by simulating the process of searching food by ants, so that the ant colony optimization algorithm has better global search capability and robustness, and is widely focused. However, in the process of path planning, the algorithm is influenced by factors such as driving environment and algorithm defects, on one hand, ants are easy to fall into a deadlock state, namely fall into 'local optimum' in the searching process, and on the other hand, the traditional ant colony algorithm lacks effective guidance on a high-quality path, so that the path is tedious.

Aiming at the problem that the path planning process possibly falls into a deadlock state, a patent CN116627175A provides an improved ant colony algorithm based on a rollback mechanism and pheromone emptying, when ants fall into a deadlock point, the method disclosed by the invention rolls back one step and brings back front nodes into a tabu table, and simultaneously empties the pheromone concentration of the deadlock point to guide the ants to escape from a deadlock area, and a patent No. CN115560774B provides an improved ant colony algorithm based on a grading punishment of the rollback steps, wherein the pheromone punishment is applied according to the rollback steps of the ants so as to reduce the attraction of the deadlock path. Patent number CN202310015209.4 proposes an improved ant colony algorithm based on deadlock path segmentation updating, and the method divides the paths of deadlock ants into dominant paths and inferior paths, respectively carries out pheromone rewarding and punishment, so that the path exploration diversity is improved, however, the solutions adopt fixed rules to treat deadlock, but due to the fact that punishment intensity lacks an adaptive adjustment mechanism, excessive punishment or insufficient punishment of the algorithm is easy to occur in a complex environment, and the defects of long paths and the like caused by poor convergence stability and weak adaptability exist.

Aiming at the problem of long paths caused by the lack of effective guidance of high-quality paths in the conventional ant colony algorithm in the path planning process, a elite ant strategy based on contribution screening is provided by a patent number CN118927239A to singly reward elite paths, a cooperative strategy of mixed ant colony and genetic algorithm is adopted by a patent number CN111860754B, delta penalty values are introduced in pheromone updating to reward elite ants exceeding a global optimal solution, however, although the schemes are innovated in elite layering and mixed optimization, the guidance of the elite paths is still concentrated on partial individuals or single optimal solution, and the effective guidance of the high-quality paths cannot be well achieved.

Therefore, the current path planning method based on the ant colony optimization algorithm still has poor performance in a complex environment, and how to realize efficient, safe and reliable path planning is still an important subject to be overcome by the unmanned vehicle.

Disclosure of Invention

The embodiment of the application provides an automobile path planning method based on a dynamic pheromone punishment coefficient and application thereof, aiming at the problems that an ant colony optimization algorithm possibly falls into a deadlock state in path planning and the problem that a path is long due to the lack of effective guidance on a high-quality path, a fuzzy controller is introduced to adjust the punishment coefficient in real time and a collaborative rewarding mechanism based on group consensus so as to improve the path convergence stability and optimize the path quality and improve the path planning capability of the ant colony optimization algorithm in a complex environment.

In a first aspect, an embodiment of the present application provides a method for planning a path of an unmanned vehicle based on a dynamic pheromone penalty coefficient, including the steps of:

S1, constructing a grid map between a navigation starting point and a navigation end point, wherein the grid map comprises a feasible region and an infeasible region;

S2, taking a navigation starting point as a starting point and a navigation end point as an end point, performing multi-generation iterative search based on an ant colony algorithm in a grid map to obtain a navigation path, wherein after each generation of iteration is finished, for ants in a dead lock state, calculating iteration progress and dead lock occurrence frequency of the current ants, inputting the iteration progress and the dead lock occurrence frequency into a fuzzy controller, outputting punishment coefficients, substituting the punishment coefficients into a punishment formula of the current ants to obtain punishment pheromone punishment of edges corresponding to the last two steps of the current ants, for ants in a dead lock state, deleting all redundant nodes in an original path of the current ants to construct a new path, calculating path optimization pheromone rewards added to each edge on the new path based on the new path and the original path, obtaining a concentrate path set according to all paths after each generation of iteration is finished, obtaining common-known edges of at least two concentrate paths based on the concentrate path set, calculating the punishment weights of the common-known edges in the concentrate paths, calculating concentrate co-known pheromones of the common-known edges based on the co-known weights, and introducing the punishment pheromones into a corresponding edge rewarding formula.

In a second aspect, an embodiment of the present application provides an unmanned vehicle path planning system based on a dynamic pheromone penalty coefficient, including:

The map construction module is used for constructing a grid map between a navigation starting point and a navigation end point, wherein the grid map comprises a feasible region and an infeasible region;

The path planning module is used for carrying out multi-generation iterative search in the grid map based on an ant colony algorithm to obtain a navigation path by taking a navigation starting point as a starting point and taking a navigation ending point as an ending point, wherein after each generation of iteration is finished, the iteration progress and the deadlock occurrence frequency of the current ant are calculated, the iteration progress and the deadlock occurrence frequency are input into the fuzzy controller to output a punishment coefficient, the punishment coefficient is substituted into a punishment formula of the current ant to obtain a punishment pheromone punishment of the edge corresponding to the last two steps of the current ant, all redundant nodes in an original path of the current ant which is not in the deadlock state are deleted to construct a new path, path optimization pheromone rewards of each edge are calculated and added to the new path based on the new path and the original path, a common recognition edge of at least two elite paths is obtained based on the elite path set, the cooperative weight of the common recognition edge in the elite path set is calculated, and the punishment pheromone and the punishment information prime factors of the corresponding edge and the punishment information factors are introduced into the optimization path rewards formula.

In a third aspect, embodiments of the present application provide a readable storage medium having stored therein a computer program comprising program code for controlling a process to perform a process comprising a method of unmanned vehicle path planning in accordance with a dynamic pheromone penalty factor based approach.

The main contributions and innovation points of the invention are as follows:

1. The fuzzy controller is introduced to adjust penalty coefficients in real time, the dynamic penalty coefficients are used as adjustment means, penalty intensity intervals are divided according to deadlock frequency and iteration progress, penalty measures with controllable matching intensity are adopted, early-stage high-frequency deadlock strong punishment is achieved, medium-term balance exploration and convergence are achieved, late-stage occasional deadlock fine lightly is achieved, solution diversity is protected, the problem that ants are easy to fall into a deadlock state in a searching process is solved, accurate guidance is further provided for adjustment of the subsequent ant searching direction, self-adaptive deadlock processing is achieved, the capability of an algorithm for escaping from local optimum is remarkably improved, and the planned path is better.

2. The collaborative rewarding mechanism based on group consensus is introduced, the common characteristics of multiple elite paths of the same generation are focused, the frequency of each node passing through the elite paths together is counted, collaborative weights are calculated according to the consensus intensity, and additional pheromone rewarding is carried out on the consensus nodes, so that the group wisdom is utilized to effectively guide the high-quality paths, accidental misleading and local optimal risks of a single path are effectively avoided, accidental misleading of a single solution is avoided, the risks of trapping in local optimal are reduced, and the high-quality paths are effectively guided.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a flow chart of a method of unmanned vehicle path planning based on dynamic pheromone penalty coefficients according to an embodiment of the application;

fig. 2 is a logical schematic of the scheme for calculating path optimization pheromone rewards and deadlock pheromone penalties.

Fig. 3 is a schematic view of an adjacent grid of the present solution.

Fig. 4 is a schematic view of a multiple U-shaped barrier environment.

Fig. 5 is a trajectory planning path diagram of a conventional ant colony algorithm.

Fig. 6 is a simulation diagram of the ant colony algorithm of the present embodiment.

Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with aspects of one or more embodiments of the present description as detailed in the accompanying claims.

It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be described as being split into multiple steps in other embodiments, while multiple steps described in this specification may be described as being combined into a single step in other embodiments.

Example 1

As shown in fig. 1, the scheme provides an unmanned vehicle path planning method based on dynamic pheromone punishment coefficients, which comprises the following steps:

In step S1, the feasible region of the grid map refers to a region where the vehicle can travel, the infeasible region refers to a region where the vehicle cannot travel, and the route planning is performed by the subsequent ant colony algorithm through the division of the feasible region and the infeasible region.

In some embodiments, the navigation map is truncated based on the navigation start point and the navigation end point, and the navigation map is rasterized to obtain a plurality of grids to obtain a grid map.

The rasterization process refers to dividing the navigation map according to a×a grid size.

Further, extracting a road in the navigation map, defining a grid attribute of each grid based on a relative position relation between the road and each grid, if the current grid is positioned in the road, forming a feasible region by all grids with feasible grid attributes, if the current grid is positioned in the road, forming an infeasible region by all grids with infeasible grid attributes, and if the grids are not positioned in the road, forming the infeasible region by all grids with infeasible grid attributes.

It should be noted that, when the grid portion is located in the road, it is not feasible to define the grid attribute corresponding to the current grid. That is, only when the grid is completely within the road, the grid attribute corresponding to the current grid is feasible.

In other words, S1 of the present embodiment intercepts the range required for navigation in the high-precision map as the navigation map based on the navigation start point and the navigation end point, extracts the road from the navigation map and rasterizes the navigation map to obtain the raster map, defines all the grids as being in the road or non-road according to the road, the grids in the road constitute a feasible region that allows the vehicle to travel, and the grids in the non-road constitute an infeasible region that does not allow the vehicle to travel.

In step S2, the scheme optimizes the pheromone update formula on the basis of the conventional ant colony algorithm, and introduces deadlock pheromone punishment, elite synergistic pheromone punishment and path optimization pheromone punishment into the pheromone update formula, thereby not only avoiding the pheromone accumulation of the deadlock path, but also strengthening the high-quality direction through double forward punishment, further quickly converging to the effective path and improving the exploration capability of the better path.

In step S2, ants are used as path search agents, each grid in the grid map is used as a node, the number of ants, the maximum iteration number, the pheromone importance factor and the heuristic information importance factor are set, the ants are placed at the node where the navigation start point is located, and multi-generation iterative search is started based on a state transition probability formula, wherein the pheromone concentration in the state transition probability formula is updated according to an pheromone updating formula.

In other words, each ant is used as an independent path search agent to simulate the foraging behavior of the ants in nature, grids with feasible attributes in the grid map are used as nodes of path search, the initialization of basic parameters of ant colony search is finished first, and then multi-generation iterative search is started. After each iteration is finished, all ants complete the construction of the path from the navigation starting point to the navigation ending point, and the concentration of the pheromone is updated according to the pheromone updating formula so as to perform the iteration of the next generation.

In some embodiments, the navigation path is derived as the path of the greatest number of iterations.

The following is the equation for the state transition probability:;

where i, j is a node, allowed indicates that the grid property of the node is feasible, The pheromone concentration from the i node to the j node at the time t,,The heuristic function from the i node to the j node at the moment t is that alpha is an important factor of the pheromone and beta is an important factor of the heuristic information.

Wherein the heuristic function is:;

Wherein the method comprises the steps of Is the distance between the i node and the j node.

The pheromone updating formula is as follows:

;

Wherein the method comprises the steps of Is a preset pheromone volatilization factor, and the volatile factor is a preset pheromone volatilization factor,The pheromone concentration from the i node to the j node at the time t,The global pheromone concentration from node i to node j at time t +1,Is a process for volatilizing the pheromone,Is the new summation of pheromones from i node to j node of all ants, wherein k represents ants, m is the number of ants,The elite co-pheromone rewards for node i to node j,Optimizing pheromone rewards for i-node to j-node paths,Punishment for deadlock pheromone from i node to j node.

It should be noted that, the pheromone updating formula is to update the pheromone between the i node and the j node, and if no elite collaborative pheromone rewards, path optimization pheromone rewards or deadlock pheromone penalties exist between the i node and the j node, the corresponding value is 0.

Fig. 2 is a logic diagram of the scheme for calculating path optimization pheromone rewards and deadlock pheromone penalties.

Regarding deadlock pheromone penalty:

after each generation of iteration of the ant colony algorithm is finished, deadlock state judgment needs to be carried out on all ants, and deadlock pheromone punishment is calculated on the ants which are in the deadlock state.

Specifically, adjacent grids of the grids where the ants are currently located are checked, and if the adjacent grids all meet preset conditions, the current ants fall into a deadlock state, wherein the preset conditions are any one of exceeding the grid map boundary, being infeasible in grid attribute or having been accessed by the current ants.

It should be noted that the neighboring grid refers to all grids immediately adjacent to the current grid, i.e., possible mobile nodes of ants.

In some embodiments, the neighboring grids are top-bottom, left-right, and four diagonally-diagonal grids of the current grid.

As shown in FIG. 3, the scheme performs triple verification on each adjacent grid (1) whether the adjacent grid exceeds the boundary of the grid map, (2) whether the grid attribute is infeasible, and (3) whether the adjacent grids are accessed by the current ant, if all the adjacent grids at least do not meet one of the conditions, the feasible area of the current grid is empty, and the corresponding current ant is in a deadlock state.

Further, for ants trapped in the deadlock state, calculating the iteration progress and the deadlock occurrence frequency of the current ants, inputting the iteration progress and the deadlock occurrence frequency into the fuzzy controller to output penalty coefficients, and substituting the penalty coefficients into the deadlock pheromone penalty of the corresponding side of the last two steps of the current ants in the penalty formula of the current ants.

In some embodiments, the iteration schedule is a ratio of the current iteration number to the maximum iteration number, and the deadlock occurrence frequency is a ratio of the total number of ants and ants in the current iteration that have a deadlock state.

In some embodiments, penalty coefficient rules are defined in the fuzzy controller, and the iteration progress and the deadlock occurrence frequency are input into the fuzzy controller to be matched with the corresponding penalty coefficient rules to obtain penalty coefficients.

Specifically, the punishment coefficient rule is that when the occurrence frequency of the dead lock is larger than 0.3 and the iteration progress is smaller than 0.4, the punishment coefficient is any value in 0.8-1, when the occurrence frequency of the dead lock is not larger than 0.3 and not smaller than 0.1 and the iteration progress is not smaller than 0.4 and not larger than 0.7, the punishment coefficient is any value in 0.4-0.7, and when the occurrence frequency of the dead lock is smaller than 0.1 and the iteration progress is larger than 0.7, the punishment coefficient is any value in 0.1-0.3.

The penalty coefficient rule table of the corresponding fuzzy controller is shown in the following table one, namely the penalty coefficient rule table of the fuzzy controller

Further, substituting the punishment coefficient into the punishment formula of the current ant to obtain the punishment of the deadlock pheromone of the last two steps of the current ant, wherein the formula is as follows:

wherein Q is a preset pheromone enhancement coefficient, To the path length of ants in which a deadlock state occurs,In order to penalize the coefficients,The dead lock pheromone punishment of the edge of the last two steps of the current ant is performed, wherein the last two steps correspond to the e node to the a node.

It should be noted that, the path length of the ant in the deadlock state is obtained by directly obtaining the number of rows (i.e., the number of nodes in the path) of the path matrix of the ant.

Regarding path optimization pheromone rewards:

According to the scheme, the paths of the ants which do not fall into the deadlock state are simplified, namely whether the paths of the ants which do not fall into the deadlock state are redundant or not is judged, if the redundant nodes exist, all the redundant nodes in the original paths of the current ants are deleted to construct new paths, and the path optimization pheromone rewards are calculated based on the new paths and the original paths.

Specifically, for each ant path which does not fall into a deadlock state, traversing the three nodes on the original path in sequence from the starting point node, defining each three nodes as a first node, a second node and a third node according to the node sequence, defining the second node as a redundant node if a grid attribute is a feasible grid exists on a connecting line of the first node and the third node, and deleting all the redundant nodes in the original path of the current ant to construct a new path.

The method includes the steps that three nodes on an original path are respectively a0, a1 and a2 from a starting node, the nodes a0 and a2 are connected, if no obstacle exists on a connecting line, the node a1 is a redundant node, the node number is deleted and updated, the node a2 becomes the node a1 after the node a1 is updated, whether the redundant nodes exist in the nodes a1, a2 and a3 or not is continuously detected, and then all the nodes are traversed in sequence until all the redundant nodes are deleted to obtain a new path.

Further, in the step of calculating the path optimization pheromone reward based on the new path and the original path, a path improvement rate of the new path relative to the original path is calculated, and if the path improvement rate is greater than the improvement rate threshold, the path optimization pheromone reward is calculated based on the path improvement rate.

In some embodiments, the path improvement rate is as follows:

;

Wherein the method comprises the steps of For the length of the original path,Is the length of the new path.

It is worth to say that the range of the path improvement rate is 0-1, and the larger the path improvement rate is, the better the optimization effect is.

In some embodiments, the path optimization pheromone rewards are as follows:

Wherein the method comprises the steps of A pheromone bonus is optimized for paths to be added to edges (p, h) in the new path,For a predetermined bonus coefficient, Q is a predetermined pheromone enhancement coefficient,Is the length of the new path.

Regarding elite collaborative pheromone rewards:

The scheme provides a collaborative rewarding mechanism based on group consensus, wherein the mechanism selects elite paths of each generation, identifies consensus edges shared by the elite paths, and rewards collaborative pheromones according to consensus strength.

In some embodiments, all paths are ordered from short to long by path length, and the multiple paths of the preamble are obtained as an elite path set.

In some embodiments, the first 20% of paths are obtained as an elite path set.

The path in this case is a path after the redundancy process.

In some embodiments, the number of times each edge is traversed by elite paths in the elite path set is calculated, if the number of times is greater than 2, the current edge is defined as a consensus edge, and the cooperative weight of each consensus edge in the elite path set is calculated as follows:

Wherein the method comprises the steps of For the number of consensus edges (c, w), m is the number of elite paths in the elite path set,For the synergistic weight of the weights,The larger it means that the current consensus edge is endorsed by more elite paths.

In some embodiments, the formula for calculating elite collaborative pheromone rewards is as follows:

Wherein the method comprises the steps of For a predetermined synergistic rewarding coefficient, Q is a predetermined pheromone constant,For the average length of all elite paths in the elite path set,Rewards for elite co-pheromones added to node c through node w on the consensus.

In order to verify the effectiveness of the method, MATLAB software is used for simulating a traditional ant colony algorithm and the ant colony algorithm of the scheme under a multi-U-shaped obstacle environment, the multi-U-shaped obstacle environment is shown in fig. 4, an obtained track planning path diagram of the traditional ant colony algorithm is shown in fig. 5, and a simulation diagram of the ant colony algorithm of the scheme is shown in fig. 6.

As shown by simulation results, the path planned by the traditional ant colony algorithm often contains a large number of redundant nodes, so that the path redundancy is not smooth, and compared with the path length planned by the traditional ant colony algorithm, the path planned by the scheme is obviously shortened.

Example two

Based on the first embodiment, the scheme provides an unmanned automobile path planning system based on dynamic pheromone punishment coefficients, which comprises the following components:

The contents of the second embodiment, which are the same as those of the first embodiment, will not be repeated here.

Example III

The present embodiment also provides an electronic device, referring to fig. 7, comprising a memory 404 and a processor 402, the memory 404 having stored therein a computer program, the processor 402 being arranged to run the computer program to perform the steps of any one of the above embodiments of the unmanned vehicle path planning method based on dynamic pheromone penalty factors.

In particular, the processor 402 may include a Central Processing Unit (CPU), or an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, abbreviated as ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application. The memory 404 may include, among other things, mass storage 404 for data or instructions. Memory 404 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions for execution by processor 402.

The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any of the unmanned vehicle path planning methods of the above embodiments based on dynamic pheromone penalty factors.

Optionally, the electronic apparatus may further include a transmission device 406 and an input/output device 408, where the transmission device 406 is connected to the processor 402 and the input/output device 408 is connected to the processor 402.

The input-output device 408 is used to input or output information. In this embodiment, the input information may be a navigation start point, a navigation end point, etc., and the output information may be a navigation path, etc.

Alternatively, in the present embodiment, the above-mentioned processor 402 may be configured to execute the following steps by a computer program:

S2, carrying out multi-generation iterative search in a grid map based on an ant colony algorithm to obtain a navigation path by taking a navigation starting point as a starting point and taking a navigation end point as an end point, wherein after each generation of iteration is finished, for ants in a dead lock state, calculating iteration progress and dead lock occurrence frequency of the current ants, inputting the iteration progress and the dead lock occurrence frequency into a fuzzy controller, outputting penalty coefficients, substituting the penalty coefficients into a penalty formula of the current ants to obtain dead lock pheromone penalties corresponding to the last two steps of the current ants, for ants in a dead lock state, deleting all redundant nodes in an original path of the current ants to construct a new path, calculating path optimization pheromone rewards added to each side on the new path based on the new path and the original path, obtaining a concentrate path set according to all paths after each generation of iteration is finished, obtaining common edges of at least two concentrate paths based on the concentrate path set, calculating cooperative weights of the common edges in the concentrate paths, calculating concentrate cooperative pheromones of the common edges based on the cooperative weights, and rewarding the dead lock pheromones, and introducing the optimized edge information rewards into the corresponding formulas

It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.

In general, the various embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the invention is not limited thereto. While various aspects of the invention may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

Embodiments of the invention may be implemented by computer software executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets, and/or macros can be stored in any apparatus-readable data storage medium and they include program instructions for performing particular tasks. The computer program product may include one or more computer-executable components configured to perform embodiments when the program is run. The one or more computer-executable components may be at least one software code or a portion thereof. In addition, in this regard, it should be noted that any blocks of the logic flows as illustrated may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on a physical medium such as a memory chip or memory block implemented within a processor, a magnetic medium such as a hard disk or floppy disk, and an optical medium such as, for example, a DVD and its data variants, a CD, etc. The physical medium is a non-transitory medium.

It should be understood by those skilled in the art that the technical features of the above embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.

The foregoing examples illustrate only a few embodiments of the application, which are described in greater detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. The unmanned vehicle path planning method based on the dynamic pheromone punishment coefficient is characterized by comprising the following steps of:

2. The unmanned vehicle path planning method based on dynamic pheromone penalty factors according to claim 1, wherein adjacent grids of the grids where the ants are currently located are checked, and if the adjacent grids all meet preset conditions, the current ants are put into a deadlock state, wherein the preset conditions are any one of exceeding a grid map boundary, being infeasible in grid attributes or having been accessed by the current ants.

3. The unmanned vehicle path planning method based on dynamic pheromone penalty coefficients according to claim 1, wherein penalty coefficient rules are defined in the fuzzy controller, and iteration progress and deadlock occurrence frequency are input into the fuzzy controller to match the corresponding penalty coefficient rules to obtain penalty coefficients.

4. The unmanned vehicle path planning method based on dynamic pheromone penalty coefficients of claim 1, wherein the deadlock pheromone penalty of the last two steps of the current ants is:

;

wherein Q is a preset pheromone enhancement coefficient, To the path length of ants in which a deadlock state occurs,In order to penalize the coefficients,Punishment is performed on deadlock pheromone of the edges of the last two steps of the current ants.

5. The unmanned vehicle path planning method based on dynamic pheromone penalty factors according to claim 1, wherein for each path of ants not involved in deadlock state, the node sequence from the starting node traverses three nodes on the original path in turn, for each three nodes, the first node, the second node and the third node are defined according to the node sequence, if a grid with feasible grid attribute exists on the connection line of the first node and the third node, the second node is defined as a redundant node, and all redundant nodes in the original path of the current ants are deleted to construct a new path.

6. The unmanned vehicle path planning method based on dynamic pheromone penalty coefficients of claim 1, wherein a path improvement rate of the new path relative to the original path is calculated, and if the path improvement rate is greater than the improvement rate threshold, a path optimization pheromone prize is calculated based on the path improvement rate.

7. The unmanned vehicle path planning method based on dynamic pheromone penalty coefficients of claim 1, wherein the path optimization pheromone rewards are as follows:;

;

Wherein the method comprises the steps of For the length of the original path,R is the path improvement rate for the length of the new path;

Wherein the method comprises the steps of Pheromone rewards are optimized for paths to be added to edges in the new path,For a predetermined bonus coefficient, Q is a predetermined pheromone enhancement coefficient,Is the length of the new path.

8. The unmanned vehicle path planning method based on dynamic pheromone penalty coefficients of claim 1, wherein the elite collaborative pheromone rewards are formulated as follows:;

Wherein the method comprises the steps of For the number of consensus edges (c, w), m is the number of elite paths in the elite path set,For the synergistic weight of the weights,For a predetermined synergistic rewarding coefficient, Q is a predetermined pheromone constant,For the average length of all elite paths in the elite path set,Rewards for elite co-pheromones added to nodes c through w on the common edge.

9. An unmanned vehicle path planning system based on dynamic pheromone penalty coefficients, comprising:

10. A readable storage medium, characterized in that the readable storage medium has stored therein a computer program comprising program code for controlling a process to execute a process comprising the unmanned vehicle path planning method based on dynamic pheromone penalty factors according to any of claims 1 to 8.