Image-Based Visual Servoing for Enhanced Cooperation of Dual-Arm Manipulation

TL;DR

This paper introduces an image-based visual servoing control method to improve the cooperation and pose synchronization of dual-arm robots, reducing errors caused by kinematic inaccuracies and enhancing manipulation stability.

Contribution

It presents a novel visual servoing approach that uses onboard cameras to measure and adapt end-effector poses, improving dual-arm cooperation over traditional kinematic-based methods.

Findings

Reduces pose synchronization errors in dual-arm systems

Enhances robustness to kinematic inaccuracies

Proves stability through theoretical analysis and real-robot experiments

Abstract

The cooperation of a pair of robot manipulators is required to manipulate a target object without any fixtures. The conventional control methods coordinate the end-effector pose of each manipulator with that of the other using their kinematics and joint coordinate measurements. Yet, the manipulators' inaccurate kinematics and joint coordinate measurements can cause significant pose synchronization errors in practice. This paper thus proposes an image-based visual servoing approach for enhancing the cooperation of a dual-arm manipulation system. On top of the classical control, the visual servoing controller lets each manipulator use its carried camera to measure the image features of the other's marker and adapt its end-effector pose with the counterpart on the move. Because visual measurements are robust to kinematic errors, the proposed control can reduce the end-effector pose synchronization errors and the fluctuations of the interaction forces of the pair of manipulators on the move. Theoretical analyses have rigorously proven the stability of the closed-loop system. Comparative experiments on real robots have substantiated the effectiveness of the proposed control.