Analysis of Path Planning Algorithms for Swarm Robots

This chapter delves into the analysis of path planning algorithms for swarm robots, focusing on the performance of A* and Dijkstra algorithms in both static and dynamic environments. The study evaluates the scalability, efficiency, and adaptability of these algorithms for real-time path planning in swarm configurations. Key topics include the architecture of the navigation framework, the use of URDF for robot modeling, and the role of odometry in tracking robot position and orientation. The chapter also explores various sensors for distance measurement, the TF tree for managing coordinate frames, and the SLAM toolbox for simultaneous localization and mapping. Additionally, it discusses the global and local costmaps for path planning, the Nav2 framework for autonomous navigation, and the Groot2 framework for developing autonomous robotic systems. The results highlight the superior efficiency of the A* algorithm in static environments and the reliability of Dijkstra's algorithm in dynamic conditions. The study concludes by emphasizing the need for improved adaptability in path planning algorithms and suggests future research directions, including the integration of machine learning to enhance real-time adaptability and efficiency in swarm robotic systems.