HPS-RRT*: An Improved Path Planning Algorithm for a Nonholonomic Orchard Robot in Unstructured Environments

Abstract

Path planning is a fundamental challenge for autonomous robots, particularly in unstructured environments, where issues such as low search efficiency, suboptimal path quality, and local optima often arise. To address these challenges and enable a nonholonomic orchard robot to accomplish tasks safely and efficiently, this paper proposes a novel HPS-RRT* algorithm based on hybrid exploration and optimization mechanisms to enhance path planning performance. A hybrid sampling strategy adapted to the environmental characteristics is proposed to improve the search efficiency, and an extended step size based on Lévy distribution is designed to balance exploration and optimization. Moreover, a pruning strategy is incorporated to reduce redundant points during the search process, enhancing the efficiency of the exploration tree and reducing unnecessary expansion. Furthermore, a novel leader-based sparrow optimization algorithm is proposed to ensure that the planned path is suitable for the nonholonomic orchard robot. It can overcome the limitations of traditional smoothing methods by simultaneously optimizing curvature and path length. Compared with existing RRT*-based algorithms in environments of varying complexity, the proposed HPS-RRT* reduces the final path length by 1.7% to 27%, improves planning efficiency by 77.7% to 93.3%, and enhances path smoothness by 27.9% to 41.7%, while maintaining a 100% success rate. Furthermore, its feasibility for a nonholonomic orchard robot is validated through a multi-target planning task with curvature constraints.