Robotic Perception-motion Synergy for Novel Rope Wrapping Tasks

TL;DR

This paper presents a general robotic method for autonomously wrapping deformable ropes around rigid objects using real-time perception and feedback, applicable to various industrial tasks.

Contribution

It introduces a novel, perception-based approach that does not require prior object knowledge, enabling versatile and high-quality rope wrapping by robots.

Findings

Wrapping quality improved and converged within 5 wraps

Method tested on 6 different rope-rod combinations

No prior physical or geometrical knowledge needed

Abstract

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.