RRT*_Heuristic_Adaptiv_maxR: A practical path planning method based on novel adaptive framework for autonomous mobile agent

Aim at addressing the problem of random sampling and low efficiency of rapidly-exploring random tree (RRT) path planning algorithm, a practical path planning method is proposed for autonomous mobile agents, named RRT*_Heuristic_Adaptiv_maxR. This method is based on a new adaptive framework, balancing the search efficiency and convergence accuracy of path planning by dynamically adjusting the maximum radius parameters in the search process. Combining the heuristic search strategy, guiding the sampling direction, accelerating convergence to the target area, the obstacle detection mechanism is used to optimize the path in real time to avoid collisions. Experimental results show that the proposed method, RRT*_Heuristic_Adaptiv_maxR, has higher planning success rate and path quality in complex environments compared with various RRT algorithms, and it is verified the effectiveness of the adaptive framework and the maximum radius dynamic adjustment strategy in mobile agent path planning.