On-line identification of contact dynamics in the presence of geometric uncertainties

Robots are increasingly used to perform complex tasks, which often involve interaction and contact with unstructured environments. By identifying geometric uncertainties and the dynamic behavior of the environment on-line, the autonomy of intelligent robot systems can be considerably improved. This paper considers the 2D case of an industrial robot equipped with a probe to explore an unknown environment. The goal is to estimate from the measured end-effector position, velocity and forces not only the environmental contact dynamics parameters, but also geometric parameters such as the environment position and orientation, and the position of the probe end-point with respect to the robot end-effector. To this end, a Kalman filter based algorithm is proposed, which enforces physical constraints and which is executed in an event-triggered way to improve convergence and robustness. Experimental results illustrate the viability of the proposed algorithm.