A Shared Control Framework for Enhanced Grasping Performance in Teleoperation

Remote teleoperation has shown significant advancements since the first teleoperation system was proposed by Goertz in the 1940s. In recent years, the research on shared control methodologies in which the robot assists the operators in accomplishing the desired tasks has gained extensive attention. One such important task in teleoperation is object grasping. In this paper, we propose a shared control framework to enhance the teleoperated grasping performance. The proposed framework is built upon a virtual reality device-based direct teleoperation system. In this framework, a template matching-based object point cloud compensation is introduced for multi-angle grasping pose generation. Then, the feasible grasping candidates are selected considering joint constraints-aware manipulability. Finally, the grasping assistance is achieved by trajectory blending with dynamic authority adjustment. To validate the performance of the proposed framework, we carried out experimental evaluations. The output results indicate improved grasping performance in terms of reduced task completion time, linear trajectory, and workload.