Trajectory Optimization with Geometry-Aware Singularity Avoidance for Robot Motion Planning
Luka Petrović, Filip Marić, Ivan Marković, Jonathan W. Kelly, Ivan K. Petrovic · 2021 21st International Conference on Control, Automation and Systems (ICCAS) · 2021
One of the principal challenges in motion planning for robotic arms is to ensure agility in the case of encountering unforeseeable changes during task execution. It is thus crucial to preserve the ability to move in every direction in task space, which is achieved by avoiding singularities, i.e., states of configuration space where degrees of freedom are lost. To aid in singularity avoidance, existing methods mostly rely on manipulability or dexterity indices to provide a measure of proximity to singular configurations. Recently, a novel geometry-aware singularity index was proposed that circumvents some of the failure modes inherent to manipulability and dexterity. In this paper, we propose a cost function based on this index and integrate it within a stochastic trajectory optimization framework for efficient motion planning with singularity avoidance. We compare the proposed method with existing singularity-aware motion planning techniques, demonstrating improvement in common indices such as manipulability and dexterity and showcasing the ability of the proposed method to handle collision avoidance while retaining agility of the robot arm.