A multi-strategy integrated RRT* algorithm for efficient and optimal mobile robotic path planning

Abstract

Sampling-based motion planning algorithms have been extensively adopted for the global path planning of mobile robots and industrial manipulators in complex static environments, owing to their probabilistic completeness and computational scalability in high-dimensional configuration space. However, most existing variants predominantly rely on single optimization strategy and multi-strategy fusion approaches often suffer from insufficient collaborative design among modules. These limitations hinder the ability to simultaneously balance exploration, convergence speed, and path quality, particularly in cluttered scenes with narrow passages. To address these challenges, this study proposes a novel multi-strategy integrated RRT* (M-RRT*) path planning framework that enables coordinated optimization across all modules. First, a three-layer hybrid sampling strategy is designed, combining goal-biased sampling, obstacle Gaussian sampling, and uniform global sampling to adaptively balance global exploration and local convergence efficiency. Second, an adaptive bidirectional tree expansion mechanism can dynamically adjust the two trees expansion state. Third, a three-point local shortcut optimization serves as the post-processing module to further refine the path quality. Extensive comparative experiments are conducted across three typical 2D environments: cluttered obstacles, maze, and narrow passages environments. The results demonstrate that, compared to RRT*, the M-RRT* reduces average path length by 24.1%, sampling nodes by 84.2%, average planning time by 83.0%, and maintains a 100% success rate across all test environments—notably enhancing planning reliability in complex constrained environments. When benchmarked against Informed-RRT* and B-RRT*, M-RRT* achieves a superior equilibrium between efficiency, success rate, and path quality, exhibiting more pronounced comprehensive advantages in complex maze and narrow passage scenarios. These quantitative results validate that the M-RRT* surpasses existing baselines in overall performance and holds application potential for automated guided vehicles, inspection robots, and industrial logistics equipment.