<scp>QS</scp> ‐ <scp>RRT</scp> *: An Improved Path Planning Algorithm with Goal‐Biased Strategy

The rapidly‐exploring Random Tree (RRT) algorithm, based on sampling, is a popular algorithm for path planning problems. The optimal RRT (RRT*) is an extension of the RRT algorithm, which provides asymptotic optimality. However, RRT* still suffers from slow convergence and high initial solution cost. To address these issues, this paper proposes QS‐RRT*, an improved version of the RRT* algorithm. QS‐RRT* can generate a better initial solution and converge to the optimal solution more quickly. Second, QS‐RRT* not only considers a set of vertices around the new node but also takes its ancestor nodes into account up to a given depth, thereby expanding the number of potential parent nodes and generating a path with lower cost. After obtaining a high‐quality initial solution, intelligent sampling is used to generate sampling points near obstacles, further reducing the path cost. The algorithm was compared with Q‐RRT* and RRT*‐Smart through a simulation platform, demonstrating the advantages of QS‐RRT* in terms of initial solution quality and convergence speed. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.