A Framework for Manipulating Deformable Linear Objects by Coherent Point Drift

Te Tang, Changhao Wang, Masayoshi Tomizuka

Abstract: Manipulation of deformable linear objects is a challenging task for robots. These objects have infinite-dimensional configuration space and are computational-expensive to model, making it difficult for real-time tracking, planning and control. To deal with these challenges, a uniform framework that includes state estimation, task planning, and trajectory planning is proposed in this letter based on the concept of coherent point drift (CPD). A real-time observer is proposed to estimate the states of deformable objects from the perceived point clouds. An online task planner is then developed to recognize the manipulation step according to the state estimation result. For trajectory planning, human operators first train robots example trajectories given several object states. In the test stage, a new feasible trajectory can be autonomously generated by a smooth transformation from training scenarios to test scenarios. A series of rope manipulation experiments on a dual-arm robotic platform are performed to validate the effectiveness of the proposed methods.