Hybrid Task Constrained Incremental Planner for Robot Manipulators in Confined Environments

Incremental motion planning has emerged as a powerful approach for generating safe trajectories in confined environments. However, its effectiveness can be hampered by susceptibility to local optima. This vulnerability arises from the algorithm's heavy dependence on the previously achieved configuration (pose) as a reference for the next step. This paper presents a novel incremental motion planning approach for redundant robot arms. It leverages an optimization-based planner combined with null-space exploration to escape local optima and generate high-quality trajectories satisfying task and collision constraints. Our approach is evaluated in an onsite polishing scenario with various robot and workpiece configurations, demonstrating significant improvements in trajectory quality compared to existing methods. The proposed approach has the potential for broad applications in industrial tasks involving redundant robot arms.