A Method Integrating RRT and A-Star Algorithms to Enhance Obstacle Navigation and Optimization

This paper addresses the issue of path planning for robots in complex environments by proposing a hybrid path planning method that integrates the Rapidly Exploring Random Trees Algorithm(RRT) and the A-STAR Search Algorithm(A-Star). The method leverages the rapid exploration capability of the RRT algorithm to generate an initial path, and combines it with the heuristic strategy of the A-Star algorithm to optimize the path, particularly in the areas near obstacles. Experimental results show that, compared to using RRT or A-Star algorithms alone, the proposed hybrid algorithm can generate shorter, smoother, and near-optimal paths while maintaining planning efficiency. Specifically, the hybrid algorithm demonstrates good performance in terms of path length, computation time, and path smoothness. Future work will focus on further optimizing the algorithm to enhance its adaptability in more complex environments and considering its application in dynamic obstacle environments.