Assistance in Teleoperation of Redundant Robots through Predictive Joint Maneuvering

Abstract

In teleoperation of redundant robotic manipulators, translating an operator’s end effector motion command to joint space can be a tool for maintaining feasible and precise robot motion. Through optimizing redundancy resolution, the control system can ensure the end effector maintains maneuverability by avoiding joint limits and kinematic singularities. In autonomous motion planning, this optimization can be done over an entire trajectory to improve performance over local optimization. However, teleoperation involves a human-in-the-loop who determines the trajectory to be executed through a dynamic sequence of motion commands. We present two systems, PrediKCT and PrediKCS, for utilizing a predictive model of operator commands in order to accomplish this redundancy resolution in a manner that considers future expected motion during teleoperation. Using a probabilistic model of operator commands allows optimization over an expected trajectory of future motion rather than consideration of local motion alone. Evaluation through a user study demonstrates improved control outcomes from this predictive redundancy resolution over minimum joint velocity solutions and inverse kinematics-based motion controllers.