Motion Planning for an Elastic Rod Using Contacts

The contribution of this article is to propose an approach that solves the motion planning problem for an extensible elastic rod using contacts with the environment. We first show how motion planning for a deformable rod can be done by coupling both quasi-static and dynamic rod models with sampling-based methods. Sampling directly in the submanifold of static equilibrium and contact-free configurations allows to take advantage of the dynamic model to improve the exploration of the state space. Then, thanks to the contact information (point, forces, direction, and the number of contacts), the exploration of the rapidly exploring random tree (RRT) approach can be improved. We present a new RRT-SLIDE algorithm, which guides the roadmap extension with a sliding contact mode based on some principles of human reasoning. We show that our approach is probabilistically complete. We also demonstrate the necessity of considering contacts on complex scenarios with several simulation experiments. Besides its performances, our algorithm does not require further tuning phase for a new scenario.