Research on Path Planning of Mobile Robots Based on A*and B* Algorithms

Abstract

Efficient path planning is of great significance for mobile robots used in various applications such as logistics, healthcare, and autonomous navigation. However, traditional path planning algorithms such as A* often face limitations in computational efficiency and heuristic selection, which hinder their effectiveness in complex scenarios. This study addresses these challenges by implementing the A* and B* algorithms, comparing their performance across various heuristics: Euclidean distance, Manhattan distance, Chebyshev distance, and Octile distance. Therefore, a series of experiments are performed to analyze path length and computation time. The results show that the Euclidean heuristic consistently produces the fastest computation times, while the B* algorithm can significantly improve the performance of some heuristics. This study highlights the importance of heuristic selection in optimal path planning and provides valuable insights into advancing mobile robot navigation systems by providing insights into heuristic optimization of path planning.