发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无通信协作运输中领导者无法区分跟随者个体反应的问题，提出一种跟随者控制律，使领导者能从聚合扳手测量中恢复障碍物位置，并给出精确恢复条件与自适应探测程序，仿真验证了其性能。
AI 中文摘要
我们研究了在无显式机器人间通信的情况下，一组机器人协作运输刚性载荷时的障碍物定位问题。领导者机器人指挥载荷的运动，而跟随者机器人协助运输并对局部检测到的障碍物作出反应。领导者测量跟随者施加在载荷上的聚合扳手（即它们施加的合力与力矩），但无法直接区分它们的个体反应。我们设计了一种跟随者控制律，使领导者能够从这些测量中恢复障碍物的位置。每个跟随者都会抵抗朝向附近障碍物的运动，导致载荷的平移和角速度与聚合扳手之间呈现分段线性关系。相邻线性区域之间的变化揭示了障碍物的方位和距离，并识别出作出反应的跟随者。我们给出了在固定载荷配置下精确恢复的充分条件，并开发了一种自适应探测程序，领导者通过向载荷施加平移和旋转输入来获得所需的测量数据。我们在仿真中展示了所提方法的性能。
英文摘要
We consider obstacle localization for a team of robots cooperatively transporting a rigid payload without explicit inter-robot communication. A leader robot directs the payload's motion, while follower robots assist and react to locally detected obstacles. The leader measures the followers' aggregate wrench, i.e., the combined force and torque they exert on the payload, but cannot directly distinguish their individual reactions. We design a follower control law that allows the leader to recover obstacle locations from these measurements. Each follower resists motion toward nearby obstacles, resulting in a piecewise-linear relationship between the payload's translational and angular velocity and the aggregate wrench. Changes between adjacent linear regions reveal an obstacle's bearing and distance and identify the responding follower. We give sufficient conditions for exact recovery at a fixed payload configuration and develop an adaptive probing procedure in which the leader applies translational and rotational inputs to the payload to obtain the required measurements. We demonstrate the performance of the proposed method in simulations.
CommentsSubmitted to the 2027 American Control Conference (ACC)
