Task Capability Improvement Algorithm for Collaborative Manipulators

2026-05-17

AI Summary

This study addresses the performance limitations in collaborative robotic manipulation caused by unintended moments arising from force application points deviating from an object’s center of mass. The work innovatively repurposes these undesired moments as beneficial supplementary control resources and proposes a torque-based collaborative control algorithm grounded in moment analysis. A multi-arm cooperative dynamics model is established to jointly optimize task capability, resource allocation, and fault tolerance. Simulation results demonstrate that the proposed approach improves overall task performance by 5.86% compared to conventional methods.

Abstract

This work introduces a cooperative task capability improvement utilizing additional moments. The manipulators apply forces at the object's grasp point. Applying forces at a point other than the object's center of gravity produces undesired moments. The undesired moment acts as an additional moment. It improves the capability of an individual manipulator and, hence, the entire collaborative group. Any improvements in task capability directly add up to the object and transportation capability. The group's enhanced capability also helps achieve optimal capability, optimal resource allocation, and maximum fault tolerance in object manipulation. Our simulation results show an improvement in the capability of 5.86 \% compared to when no moment is used to enhance the capability of the manipulators.

Problem

• collaborative manipulators
• task capability
• undesired moments
• object manipulation
• force application

Innovation

• collaborative manipulators
• task capability improvement
• additional moments
• optimal resource allocation
• fault tolerance