Optimization of Robotic Path Planning Algorithms for Autonomous Material Handling Systems - Amit Shishodia (International Journal of Robotics and Automation in Mechanics, 2024)
For autonomous systems for handling materials (AMHS) to operate as efficiently as possible in industrial and logistical settings, robotic route planning is essential. This study examines many robotic route planning algorithms, emphasizing their use, ways of optimization, and difficulties in material handling systems. To improve the effectiveness, precision, and computational viability of these algorithms, the study also examines a number of optimization strategies, including machine learning, parallelization, heuristic search, and real-time adaptation approaches. The difficulties in designing path planning algorithms are specifically discussed, especially in dynamic and uncertain situations where the task needs and barriers are constantly shifting. We offer insights into how multiple approaches, including the A* algorithm, rapidly exploring random tree (RRT), Dijkstra’s algorithm, and machine learning-based techniques, may be modified for accuracy, efficiency, and real-time adaptability. To optimize path planning for dynamic situations, the study also examines the trade-offs that must be made between cutting computing complexity, improving real-time decision-making, and consuming the least amount of energy. Future directions in path planning optimization are discussed in the conclusion of the research, including the use of swarm robots, artificial intelligence (AI), and multi-agent systems in unmanned material handling systems. The trade-offs between computing complexity and path optimality are also examined, as is the effect of path planning on system performance, safety, and energy efficiency. To better enhance material handling systems autonomously, the study finishes by outlining new developments in the field, such as the integration of AI, multi-agent coordination, and swarm robotics. Finally, this work supports the shift to fully computerized and intelligent material handling operations by offering a thorough knowledge of how path planning algorithms may be improved to increase AMHS’s efficacy and adaptability in a variety of industrial contexts.
Keywords: Logistics; Machine Learning; Navigation; Optimization; Robotic path planning; algorithms; artificial intelligence; autonomous material handling; industrial automation