Detection and Physical Interaction with Deformable Linear Objects
This work addresses the challenge of automating perception and manipulation of deformable objects for robotics applications, representing an incremental advancement by extending existing methods to aerial robots.
The paper tackles the problem of detecting and physically interacting with deformable linear objects (e.g., cables) by developing a method that automatically extracts initial conditions from segmentation outputs to enable tracking, even with crossings and occlusions, and demonstrates its application in routing and manipulation tasks with ground and aerial robots, achieving feasibility in aerial manipulation.
Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. However, perception of these objects and the study of physical interaction with them is still a growing area. There have already been successful methods to model and track deformable linear objects. However, the number of methods that can automatically extract the initial conditions in non-trivial situations for these methods has been limited, and they have been introduced to the community only recently. On the other hand, while physical interaction with these objects has been done with ground manipulators, there have not been any studies on physical interaction and manipulation of the deformable linear object with aerial robots. This workshop describes our recent work on detecting deformable linear objects, which uses the segmentation output of the existing methods to provide the initialization required by the tracking methods automatically. It works with crossings and can fill the gaps and occlusions in the segmentation and output the model desirable for physical interaction and simulation. Then we present our work on using the method for tasks such as routing and manipulation with the ground and aerial robots. We discuss our feasibility analysis on extending the physical interaction with these objects to aerial manipulation applications.