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

2024-10-25

arXiv.org

AI Summary

To address end-effector pose desynchronization and interaction force fluctuations in dual-arm cooperative manipulation—caused by kinematic modeling errors and joint measurement biases—this paper proposes an image-based visual servoing (IBVS)-enhanced cooperative control method. We innovatively embed IBVS into a closed-loop dual-arm coordination framework for the first time, enabling cross-arm adaptive pose alignment via real-time image features of end-effector markers. By integrating a coupled dynamic model of both arms with Lyapunov stability analysis, we rigorously prove global asymptotic stability of the closed-loop system. Experimental validation on a physical platform demonstrates a 42% reduction in end-effector pose synchronization error and a 57% decrease in interaction force fluctuation, significantly improving positioning accuracy and operational stability in fixture-free cooperative tasks.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Enhance dual-arm robot cooperation
• Reduce end-effector pose errors
• Improve interaction force stability

Innovation

• Image-based visual servoing
• Dual-arm manipulator cooperation
• Robust visual measurements