Manipulation of Deformable Linear Objects in Benchmark Task Spaces

Abstract

Manipulation of deformable linear objects (DLOs) is a key robot capability in applications such as manufacturing, logistics, and healthcare. DLOs are commonly found in industrial and domestic environments in the form of cables, ropes, and wires. However, dexterous manipulation of these objects autonomously is computationally expensive due to their infinite degrees of freedom in 3-D space. Manipulation of DLOs is also typically constrained by the environment, which poses additional challenges due to restricted robot motions. In this study, we propose a method for automating the manipulation of DLOs in constrained spaces. The approach contains a geometric model of cable-like objects based on multiple features including point cloud, color, and shape. Given the estimated 3-D cable model and environment information, we propose a model-based planning methodology for manipulating DLOs in constrained spaces. We demonstrate the efficiency and robustness of our method by automating a cable threading task with real robot experiments using an assembly task board designed by the National Institute of Standards and Technology.