The optimal rapidly-exploring random tree(RRT*)algorithm is usually used in complex environments for robot path planning.However,it has issues of blind search,redundant nodes and long path length.An improved RRT*algorithm(AF-RRT*)that combined tree expansion strategy with sampling strategy was proposed.To reduce the path length,parent node creation strategy was used to improve the structure of the RRT*extension tree.The adaptive exploration strategy was in-troduced to increase the sampling-oriented selectivity and reduce the path search time without falling into the local optimum trap.The dynamic step size was utilized to decrease the redundant nodes.Simulation results show that AF-RRT*algorithm is better than RRT*and F-RRT*in path acquisition efficiency and path quality.The ablation experiment verifies the effectiveness of AF-RRT*algorithm and its function modules.
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