Enhanced APF-RRT for Motion Planning and Optimization of Orchard Harvesting Manipulators

In orchard harvesting, complex occlusions, dense obstacles, and limited workspace make manipulator motion planning an autonomous decision-making problem in unstructured environments, requiring collision avoidance while balancing efficiency and trajectory smoothness. However, traditional Artificial Potential Field (APF) and Rapidly-Exploring Random Tree (RRT) algorithms have limitations in meeting harvesting demands. To address this, an enhanced APF-RRT algorithm is proposed via three improvements: potential field modeling, sampling expansion, and path optimization—an exponential smooth attractive field and a non-linear repulsive field to improve potential-field continuity and guidance stability; an obstacle-distance-based dynamic expansion strategy for rapid search and precise avoidance, reducing redundant nodes; and cubic B-spline smoothing to enhance path quality and motion stability. Validation was conducted under laboratory-simulated orchard obstacle conditions rather than in actual orchards. Compared with traditional RRT, the enhanced algorithm reduces planning time, path length, and sampling nodes by 83.55%, 18.04%, and 81.20% in 2D and by 89.77%, 20.10%, and 88.74% in 3D. Further manipulator experiments show reductions in sparse scenarios of 20.38%/21.22%, 19.71%/20.02%, and 18.46%/18.74% (single-/multi-category) and in dense scenarios of 21.62%/18.57%, 21.20%/19.65%, and 20.36%/18.74%. Results demonstrate improved planning efficiency, shorter paths, and enhanced motion stability, validating the algorithm’s generalization and adaptability.