Progress-driven sampling and multi-channel extension for bidirectional manipulator path planning in constrained environments

Abstract

Constrained close-proximity service scenarios, exemplified by bedside rehabilitation, require collaborative manipulators to operate safely in cluttered human-robot shared spaces with dense obstacles and narrow passages. To address the low sampling efficiency and slow convergence of multi-DoF manipulator path planning in such constrained environments, this paper proposes a bidirectional algorithm named Progress-driven Multi-Channel RRT* (PMC-RRT*). This method integrates a progress-driven sampling approach with a multi-channel extension technique. First, a progress-driven sampling scheme is proposed to adaptively shift the sampling center and shrink the sampling domain to balance global exploration and local exploitation as the search progresses. Second, a multi-channel extension strategy is developed to combine direct steering, spherical shell preferential detouring, and potential field-based tangential sliding to enhance search guidance and obstacle avoidance flexibility. We also create a whole-body collision checking model based on the manipulator’s forward kinematics, enforcing its joint limits and a global minimum clearance constraint as a unified feasibility criteria throughout sampling, extension, and rewiring. To validate the performance of the PMC-RRT*, we conducted comparative experiments against six baseline algorithms—RRT-Connect, RRT*, Bi-RRT*, GB-RRT*, BAI-RRT*, and Bi-APF-RRT*—in terms of planning time, number of nodes, and iteration, path length, mean clearance, and mean turning angle. The results show that in most scenarios, PMC-RRT* achieved significantly lower planning time, number of nodes, and number of iterations than the baseline algorithms while maintaining comparable path length. Furthermore, PMC-RRT* also obtained larger mean clearance and smaller mean turning angle, and performed exceptionally well in cluttered and narrow bottleneck environments. Simulation and physical prototype experiments further confirm that the planned paths satisfy joint limits and safety clearance constraints. The manipulator achieves stable tracking without collision throughout the entire motion, which demonstrates robustness and physical executability in clearance-critical cluttered scenes, supporting its potential for bedside rehabilitation and other close-proximity applications.