  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 paper 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 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.
