A phase search-enhanced Bi-RRT path planning algorithm for mobile robots

Abstract

The proposed improvement to the Rapidly-exploring Random Tree (RRT) path planning algorithm is aimed at addressing the issue of slow convergence speed caused by boundary information in the original algorithm, by introducing a phase search approach. The initial approach involves employing a three-stage search strategy to generate sampling points that are specifically oriented toward real-time sampling failure rate, thereby significantly reducing the number of redundant nodes. Simultaneously, a balanced exploration strategy is introduced, enhancing the algorithmos convergence speed by constructing two randomly growing trees for searching. Secondly, a path-pruning strategy is implemented, effectively reducing the path length. Finally, the bidirectional exploration technique from the improved algorithm is applied to the traditional RRT algorithm based on boundary information, and comparative experiments are conducted. The experimental results demonstrate that, compared to the traditional boundary-based RRT method, the proposed improved algorithm reduces the running time by 13.4% and decreases the path length by 9.51%.