Enhanced RRT* Algorithm for Efficient Path Planning in Robotics and Autonomous Driving

Planning algorithms are essential for reducing computational complexity in robotics and autonomous driving. While the Rapidly exploring Random Tree Star (RRT*) algorithm offers probabilistic completeness and asymptotic optimality, its practical efficiency is hampered by slow convergence, high initial path cost, and excessive invalid sampling due to uninformed tree expansion. To address these limitations, this study introduces the A-RRT* algorithm. The key improvements include: utilizing an A* path to define an adaptive sampling region for faster initial solution quality and convergence; incorporating a goal bias strategy to guide random node generation; and implementing a steering angle criterion during parent node reselection within a multi-iteration replanning framework to refine the path to global optimality. Simulation confirm that the proposed A-RRT* algorithm effectively enhances planning efficiency and path quality compared to the comparison algorithms. Specifically, it reduces the initial solution time by up to 55%, lowers the initial path cost by 4.5–12.5%, and achieves final path cost that is 1.6–9.8% shorter.