A Fast Score-Based Method for Robotic Task-Free Point-to-Point Path Learning

The manipulation of deformable objects represents an open research topic because of the difficulties in accurately modeling the object behavior in real-world scenarios. This paper presents a trajectory planning framework for the assembly of wiring harnesses for the automotive and aerospace sector, reducing the learning time and simultaneously presenting suitable performance and reliability. A genetic algorithm is used to generate new trajectories according to application constraints. Those trajectories are then executed by the robot and evaluated by means of proper sensor feedback. The proposed framework enable to learn and autonomously improve the task execution, while mantaining a significantly low programming time. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, while showing high success rate from the very beginning of the learning phase.