Abstract
Aiming at the problems of rapid-expanding random trees (RRT) in path planning, such as strong search blindness, high randomness, slow convergence, and non-smooth generated paths, this paper proposes a Multi-Strategy Fusion RRT (MSF-RRT) algorithm to improve RRT. Firstly, a target bias strategy introduces a higher probability that the target region samples points; secondly, a bias expansion strategy expands the sampling points to the target points in an orderly manner; then, an adaptive step size strategy adjusts the expansion step size according to the map complexity. Finally, the preliminary planned path fits and optimises through pruning process and cubic B-spline curve. The simulation results show that in path planning with different map complexity, the simulation results show that the MSF-RRT algorithm reduces the search time, path length, and number of nodes by an average of 90.53%, 16.84%, and 88.43%, respectively, compared to the traditional RRT algorithm; by an average of 79.33%, 14.58%, and 77.71%, respectively, compared to the RRT-Star algorithm; and by an average of 49.74%, 14.89%, and 68.74%, respectively, compared to the RRT- Connect algorithm. The MSF-RRT algorithm shows higher efficiency and better performance in path planning and aligns with the kinematic properties of path planning.