Collaborative robots have to adapt its motion plan to a dynamic environment and variation of task constraints. Currently, they detect collisions and interrupt or postpone their motion plan to prevent harm to humans or objects. The more advanced strategy proposed in this article uses online trajectory optimization to anticipate potential collisions, task variations, and to adapt the motion plan accordingly. The online trajectory planner pursues a model predictive control approach to account for dynamic motion objectives and constraints during task execution. The prediction model relates reference joint velocities to actual joint positions as an approximation of built‐in robot tracking controllers. The optimal control problem is solved with direct collocation based on a hypergraph structure, which represents the nonlinear program and allows to efficiently adapt to structural changes in the optimization problem caused by moving obstacles. To demonstrate the effectiveness of the approach, the robot imitates pick‐and‐place tasks while avoiding self‐collisions, semistatic, and dynamic obstacles, including a person. The analysis of the approach concerns computation time, constraint violations, and smoothness. It shows that after model identification, order reduction, and validation on the real robot, parallel integrators with compensation for input delays exhibit the best compromise between accuracy and computational complexity. The model predictive controller can successfully approach a moving target configuration without prior knowledge of the reference motion. The results show that pure hard constraints are not sufficient and lead to nonsmooth controls. In combination with soft constraints, which evaluate the proximity of obstacles, smooth and safe trajectories are planned.

中文翻译：

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考虑动态障碍的协同操纵器模型预测控制

协作机器人必须使其运动计划适应动态环境和任务约束的变化。当前，他们检测碰撞并中断或推迟其运动计划，以防止对人或物体的伤害。本文提出的更高级的策略使用在线轨迹优化来预测潜在的碰撞，任务变化并相应地调整运动计划。在线轨迹规划器采用模型预测控制方法来考虑任务执行过程中的动态运动目标和约束。预测模型将参考关节速度与实际关节位置相关联，作为内置机器人跟踪控制器的近似值。基于超图结构的直接搭配解决了最优控制问题，代表非线性程序，可以有效地适应由移动障碍物引起的优化问题中的结构变化。为了演示该方法的有效性，该机器人模仿了拾取和放置任务，同时避免了包括人在内的自碰撞，半静态和动态障碍。该方法的分析涉及计算时间，违反约束和平滑度。结果表明，在对模型进行识别，减少阶数并在真实机器人上进行验证之后，具有输入延迟补偿的并行积分器在精度和计算复杂度之间取得了最佳折衷。模型预测控制器可以成功地达到运动目标配置，而无需先验参考运动。结果表明，单纯的硬约束不足以导致控制不流畅。结合评估障碍物接近程度的软约束，计划了平滑且安全的轨迹。