Dual-arm Coordinated Manipulation for Object Twisting with Human Intelligence

TL;DR

This paper introduces a hybrid control strategy for dual-arm robotic twisting tasks, combining optimized motion planning and human telecontrol to improve safety, dexterity, and efficiency.

Contribution

It proposes a novel dual-arm control method integrating automated planning and human input, addressing safety and adaptivity challenges in twisting manipulation.

Findings

Achieves collision and singularity free dual-arm operation

Enhances dexterity, safety, and efficiency in twisting tasks

Demonstrates effectiveness through analysis and simulation

Abstract

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are preliminary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.