Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This letter presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main idea is to split the planning horizon at the waypoint when it becomes reachable within the current planning horizon and reduce the horizon length towards the waypoints and goal points. This approach keeps the computational load low and provides flexibility in adapting to changing conditions in real-time. The presented approach achieves competitive path lengths and trajectory durations compared to (global) offline RRT-type planners, VP-STO, and tracking MPC in a multi-waypoint scenario. Moreover, the ability of wMPC to dynamically replan tasks online is experimentally demonstrated on a
KUKA
LBR iiwa 14 R820 robot in a dynamic pick-and-place scenario.