Model Predictive Contact Control for Human-Robot Interaction
Proceedings of the 47th International Symposium on Robotics, VDE Verlag, 2016.
47th International Symposium on Robotics - Robotics in the era of digitalisation, München, 21.-22. Juni 2016
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.