Adaptive motion control in uncertain environments using tactile feedback

Next generation robot applications are expected to leave the field of complex tasks in simple environments and move on to simple and complex tasks in complex environments. In our opinion, tactile feedback is a key technology for motion planning in such unstructured environments as visual information may be insufficient or even unavailable. In this paper, we show the performance of a tactile feedback controller in joint-space, which is not bound to the null space of the manipulator. Additionally, we extend our tactile feedback control framework to hierarchical multi-space controllers with adaptive prioritization. This allows to dissolve the trade-off between low contact forces and good positional tracking and aims at applications, where desired trajectories must be held using manipulator redundancy and end-effector deviation is only admissible at high contact forces. The stability of this approach is discussed as well. Furthermore, we present an online stiffness estimation algorithm to increase the performance of our controllers in uncertain environments. Several real-world experiments with a 9-DOF multipurpose manipulator in collision with soft and hard objects show the capability of our work.