Model Predictive Contact Control for Human-Robot Interaction

For shared human-robot workspaces and safe physical human-robot interaction, robots have to react adequately to intended and unintended contacts with their environment. In this contribution, a Nonlinear Model Predictive Control approach is presented that allows to move the robot end-effector along Cartesian trajectories while at the same time the robot reacts compliantly to contacts based on the estimated contact force. The impairment of the trajectory following task during contacts is minimized by exploiting the robot redundancy. The controller is based on the kinematic robot model. That means no exact knowledge of the robot dynamics is required and the approach is applicable to both fixed-base and mobile manipulators. In contrast to classical control strategies, joint constraints and self-collisions can be directly considered. In order to reduce the amount of unintended contacts, the control approach can also be combined with a collision avoidance extension. The developed algorithms are validated in experimental results on a KUKA LWR IV.