让机械臂在抓取未知重物时自动补偿力矩,提升搬运精度。
Wrench-Aware Admittance Control for Unknown-Payload Manipulation

- 通过腕部力矩传感器双重使用,识别并补偿重物带来的偏置力矩
- 实验表明搬运与放置误差显著降低,且保持柔顺运动特性
- 适合需要精准堆叠或处理非标重物的工业场景
未知负载会显著影响柔性机器人操作,尤其当负载质心与工具中心点(TCP)不重合时,会在机械臂手腕处产生偏置力矩。该力矩不仅与负载重量有关,还受惯性影响。若未建模,柔顺控制器可能误将其视为外部交互力,导致意料之外的柔顺运动、更大跟踪误差和运输精度下降。本文提出一种针对未知负载抓取-放置任务的力矩感知柔顺控制框架,基于UR5e机器人实现。方法利用腕部力矩传感器的双重作用:首先,在运输阶段引入三轴平移激励以削弱负载引起的力效应,同时避免机器人过刚;其次,抓取后先估计负载质量以进行运输补偿,再通过后续平移过程中采集的腕力矩数据,估计负载质心相对于TCP的偏移量,从而优化物体放置与堆叠行为。实验结果表明,相比未经校正的放置,该方法在保持柔顺性的同时显著提升了运输与放置性能。
原文摘要 · Abstract (English)
Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center point. In this case, the payload generates an offset wrench at the robot wrist. During motion, this wrench is not only related to payload weight, but also to payload inertia. If it is not modeled, the compliant controller can interpret it as an external interaction wrench, which causes unintended compliant motion, larger tracking error, and reduced transport accuracy. This paper presents a wrench-aware admittance control framework for unknown-payload pick-and-place using a UR5e robot. The method uses force-torque measurements in two different roles. First, a three-axis translational excitation term is used to reduce payload-induced force effects during transport without making the robot excessively stiff. Second, after grasping, the controller first estimates payload mass for transport compensation and then estimates the payload CoM offset relative to the TCP using wrist force-torque measurements collected during the subsequent translational motion. This helps improve object placement and stacking behavior. Experimental results show improved transport and placement performance compared with uncorrected placement while preserving compliant motion.
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