Nohaidda Sariff

By Nohaidda Sariff, Denesh Sooriamoorthy, Ahmad Shah Hizam Md Yasir, Puteri Nor Aznie Fahsyar Syed Mahadzir, Joy Massouh, Miqdad Taqi Mohamed Mushadiq, Julian Tan Kok Ping and Steven Eu Kok Seng

Robotic prostheses involve the utilization of artificial limbs designed for optimal power efficiency, significantly enhancing users’ mobility and independence. The primary focus in prosthesis development is on aspects related to power efficiency, aiming to create more advanced and energy-efficient solutions in the future. The initial discussion will delve into the state-of-the-art advancements in prosthesis robotics. Issues and challenges associated with robotic-powered prostheses, such as limited battery lifespan and power-to-weight balance concerns, will be explored. Recent approaches incorporating energy-efficient design strategies, including regenerative systems, actuation selection, power transmission mechanisms, and material selection, will also be examined. The strengths and limitations of these approaches will be highlighted. In conclusion, the presentation will outline future directions for power prosthesis robotics, addressing gaps in the current development of this field.

Part of the book: Exploring the World of Robot Manipulators

By Nohaidda Sariff, Yahya Muhammad Adam, Intan Izafina Idrus, Zool Hilmi Ismail, Puteri Nor Aznie Fahsyar, Swee King Phang, Kok Seng Eu, Md Hasan Molla and Denesh Sooriamoorthy

Deep reinforcement learning (DRL) has emerged as a prominent framework in the field of autonomous robot navigation, enabling agents to acquire complex decision-making capabilities and learn optimal policies through continuous interaction with their environment. This chapter provides a comprehensive review of deep reinforcement learning (DRL) in recent robot navigation research within real-time dynamic environments, addressing the gap caused by the limited existing reviews in this area. It begins with fundamental concepts, highlights current trends, discusses key challenges, and concludes with insights into future research directions. Current studies emphasize a shift from static to dynamic environments, improvements in sample efficiency, integration with visual perception, multi-agent systems, multi-objective navigation, and bridging the gap between simulation and real-world applications. These trends underscore the importance of enhancing robot adaptability, learning efficiency, robustness, and scalability, enabling robots to reach their targets while avoiding obstacles effectively. Significant challenges remain, including handling continuous action spaces, designing effective reward functions to balance exploration and exploitation, and addressing learning issues in both dynamic and real-world settings. These challenges will be examined in detail within this review. Furthermore, the chapter will explore future research directions, such as addressing dynamic and actively changing obstacle configurations, integrating DRL with other artificial intelligence techniques, improving learning efficiency across varying scales, and developing strategies for cooperative multi-agent systems. Throughout this review, key limitations and research gaps are identified, with the aim of advancing toward more autonomous, reliable, and scalable DRL-based navigation systems capable of operating effectively and efficiently in real-time environments.

Part of the book: Multi-Agent Systems

By Puteri Nor Aznie Fahsyar, Nohaidda Sariff and Zool Hilmi Ismail

Hybrid Renewable Energy Systems (HRES) are increasingly adopted to provide reliable electricity in rural and remote areas lacking access to centralized grids. This chapter explores the development and validation of a Multi-Agent System (MAS) framework designed to enhance the control and coordination of off-grid HRES integrating solar photovoltaic, wind turbines, battery storage, a diesel generator, and an inverter. The decentralized architecture assigns autonomous agents to each energy component, overseen by a central Control Agent that ensures optimal energy dispatch, fuel minimization, and system stability. The framework was evaluated over a 92-day period using real meteorological and load data. The MAS-based control strategy demonstrated significant improvements in operational efficiency, achieving over 90% reduction in fuel consumption and operating costs compared to conventional diesel-only systems. Solar photovoltaic emerged as the dominant energy source, with battery storage playing a key role in mitigating renewable intermittency and maintaining reliability. Economic assessment showed a 25% reduction in total system cost and a levelized cost of energy of $0.276/kWh, with an investment payback period of less than 2 years. These findings highlight the potential of MAS as an intelligent, scalable, and cost-effective solution for managing decentralized energy systems in underserved regions. The approach supports broader goals of enhancing energy access, promoting renewable integration, and advancing sustainable development in off-grid contexts.

Part of the book: Multi-Agent Systems

By Zool Hilmi Ismail, Nohaidda Sariff and Puteri Nor Aznie Fahsyar

This chapter presents a conceptual examination of the transition from current agentic artificial intelligence or agentic AI system to future AI frameworks. Special attention is given to applications that intersect directly with society, such as next generation of educational tools and the resulting impacts on workforce dynamics, human augmentation, and ethical considerations. Central to this analysis is the importance of human-centered design, ensuring that agentic AI systems remain transparent, trustworthy, and aligned with human values. Identifying key research priorities, this study seeks to guide the development of agentic AI that benefits society while mitigating risks associated with autonomy and decision-making at scale.

Part of the book: Multi-Agent Systems