面向新型绳索缠绕任务的机器人感知-运动协同
Robotic Perception-motion Synergy for Novel Rope Wrapping Tasks
AI总结：
本文提出一种基于实时RGB-D感知与反馈控制的通用机器人绳索缠绕方法，无需物体先验知识，并在3绳2杆的6种组合上验证了5圈内质量收敛。
摘要：
本文提出了一种新颖且通用的方法，用于解决使用配备平行夹爪的通用机器人机械臂自主地将可变形线性物体（DLO，称为绳索）缠绕在刚性物体（称为杆）上的问题。此类机器人缠绕任务在汽车、机电工业、建筑制造等领域具有广泛的潜在应用，但此前几乎未被研究。我们的方法不需要事先了解物体的物理和几何属性，而是使机器人能够利用实时RGB-D感知来确定缠绕状态，并通过反馈控制实现高质量的结果。因此，它为机器人机械臂提供了处理不同杆或绳索缠绕任务的通用能力。我们在3种不同绳索和2种杆的6种组合上测试了我们的方法。结果表明，在所有测试案例中，缠绕质量均在5圈以内得到改善并收敛。
英文摘要：
This paper introduces a novel and general method to address the problem of using a general-purpose robot manipulator with a parallel gripper to wrap a deformable linear object (DLO), called a rope, around a rigid object, called a rod, autonomously. Such a robotic wrapping task has broad potential applications in automotive, electromechanical industries construction manufacturing, etc., but has hardly been studied. Our method does not require prior knowledge of the physical and geometrical properties of the objects but enables the robot to use real-time RGB-D perception to determine the wrapping state and feedback control to achieve high-quality results. As such, it provides the robot manipulator with the general capabilities to handle wrapping tasks of different rods or ropes. We tested our method on 6 combinations of 3 different ropes and 2 rods. The result shows that the wrapping quality improved and converged within 5 wraps for all test cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
