用视觉反馈提升双臂机器人协同精度，减少误差和力波动。
Image-Based Visual Servoing for Enhanced Cooperation of Dual-Arm Manipulation
- 通过自携相机实时捕捉对方标记点，动态调整末端位姿
- 实验证明可显著降低末端位姿同步误差与作用力波动
- 适合需要高精度协同操作的工业场景，如装配与搬运
双臂机器人协同操作物体时无需固定夹具。传统方法依赖运动学模型和关节坐标测量来协调两机械臂末端位姿，但实际中因运动学参数不准确及关节测量误差，常导致显著的位姿同步偏差。本文提出一种基于图像的视觉伺服控制方法，使每台机械臂利用自身携带的摄像头实时观测另一臂标记点的图像特征，并据此动态调整自身末端位姿。由于视觉测量对运动学误差具有鲁棒性，所提方法有效降低了末端位姿同步误差及双臂在运动过程中的交互力波动。理论分析严格证明了闭环系统的稳定性。真实机器人对比实验验证了该控制策略的有效性。
原文摘要 · Abstract (English)
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.
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