机器人可自适应识别并补偿未知重物，实现精准抓取
Mass-Adaptive Admittance Control for Robotic Manipulators
- 结合阻抗控制与质量估计算法，动态调整驱动力
- 实验中定位精度提升，处理不同重量物体更稳定
- 适合需灵活搬运重物的工业自动化场景
在机器人操作中，处理未知或变化的质量是常见挑战，若控制系统无法实时适应，常导致误差或失稳。本文提出一种新方法，使六自由度机械臂在未知负载情况下仍能可靠跟踪路径点，并自动估计与补偿负载质量。该方法将阻抗控制框架与质量估计算法结合，动态调整激励力以补偿负载质量，有效缓解末端执行器下垂问题，保持系统稳定性。我们在带有横杆的货架上进行了具有挑战性的拾取-放置任务实验，结果表明，相比基线阻抗控制方案，本方法在到达路径点的精度和顺应性运动方面均有显著提升。通过安全容纳未知负载，本工作增强了机器人自动化的灵活性，为不确定环境下的自适应控制提供了重要进展。
原文摘要 · Abstract (English)
Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in real-time. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in a challenging pick-and-place task on a shelf with a crossbar, improved accuracy in reaching waypoints and compliant motion compared to a baseline admittance-control scheme. By safely accommodating unknown payloads, our work enhances flexibility in robotic automation and represents a significant step forward in adaptive control for uncertain environments.
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