CHiMP: A Contact based Hilbert Map Planner

This work presents a new contact-based 3D path planning approach for manipulators using robot skin. We make use of the Stochastic Functional Gradient Path Planner, extending it to the 3D case, and assess its usefulness in combination with multi-modal robot skin. Our proposed algorithm is verified on a 6 DOF robot arm that has been covered with multi-modal robot skin. The experimental platform is combined with a skin based compliant controller, making the robot inherently reactive. We implement different state-of-the-art planners within our contact-based robot system to compare their performance under the same conditions. In this way, all the planners use the same skin compliant control during evaluation. Furthermore, we extend the stochastic planner with tactile-based explorative behavior to improve its performance, especially for unknown environments. We show that CHiMP is able to outperform state of the art algorithms when working with skin-based sparse contact data.