2019/09/27 by Mincheul Kang, Kang, Mincheul, Heechan Shin +5
Computer Science · Engineering · #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotic Mechanisms and Dynamics #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1909.12517
openalex publication_date 2019/09/27 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28
A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given endeffector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving smoothness and collisionfree manipulation. Our method holistically incorporates three desired properties into the trajectory optimization process by integrating the Jacobian-based inverse kinematics solving method and an optimization-based motion planning approach. Specifically, we optimize a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To avoid falling into local minima, we iteratively explore different candidate trajectories with our local update. We compare our method with state-of-the-art methods in test scenes including external obstacles and two non-obstacle problems. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.