CIMAP: A High-Performance Motion Planning Algorithm for Robotic Manipulators in Complex Environments Using Clearance Inference Network

Abstract

This article introduces CIMAP, a high-performance motion planning algorithm for robotic manipulators in complex environments, based on the clearance inference network (CIN). CIMAP incorporates a batch collision estimation module powered by CIN, which efficiently predicts collisions by dividing the manipulator’s workspace into voxels and estimating clearances between the manipulator and surrounding obstacles. The algorithm also features a batch adaptive bidirectional expansion mechanism, enabling the simultaneous extension of multiple nodes within joint space. Leveraging CIN for batch collision estimation, CIMAP accelerates the discovery of feasible paths. Additionally, CIMAP includes a phased path optimization mechanism that identifies local shortcuts through CIN, improving path efficiency. A geometric collision checker ensures safety, performing necessary repairs when required. To assess CIMAP’s effectiveness in continuous motion planning, we compared its performance against four existing algorithms (CN-RRT, B-RRT, GB-RRT<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup>, and NPB-RRT<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup>-DC) across various obstacle scenarios. Experimental results demonstrate that CIMAP achieves an average motion planning time of under 0.7 s, improving planning efficiency by at least 89% compared to the baseline algorithms, while maintaining shorter path lengths.