A Model Predictive Control Approach to Trajectory Tracking With Human-Robot Collision Avoidance

Human-robot collision avoidance plays an essential role in facilitating the integration of robotic systems. However, many state-of-the-art approaches do not consider the future movements of dynamic obstacles or neglect the importance of allowing trajectory tracking behavior. To this end, a method based on model predictive control (MPC) and dynamic obstacle prediction is proposed that features trajectory tracking with minimal error in obstacle-free cases. Otherwise, collisions with dynamic obstacles are avoided by either only adapting the speed or by additional local deviations from the path while quickly recovering to a low tracking error afterwards. This MPC formulation is applied to an omnidirectional mobile robot within simulations and real-world experiments that demonstrate the effectiveness of the proposed approach with respect to tracking accuracy and human safety.