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.

翻译：系统性地将动态变更的路径点作为期望的离散行为（例如由高层任务规划产生的动作）纳入在线模型预测轨迹优化仍面临挑战，尤其当规划时域较短时。本文提出一种用于在线重规划任务的新型路径点模型预测控制（wMPC）概念。核心思想是在路径点进入当前规划时域后，将规划时域在该路径点处进行分割，并缩短面向路径点和目标点的时域长度。该方法在保持较低计算负荷的同时，为实时适应动态条件变化提供了灵活性。在多重路径点场景下，所提方法相较于（全局）离线RRT型规划器在路径长度与轨迹时长方面均具有竞争力。此外，通过在KUKA LBR iiwa 14 R820机械臂上开展的动态拾放实验，验证了wMPC在线动态重规划任务的能力。