Model predictive control of a collaborative manipulator considering dynamic obstacles
Model predictive control of a collaborative manipulator considering dynamic obstacles
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DOI：
10.1002/oca.2599
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发表时间：
2020-07-01
影响因子：
1.8
通讯作者：
Bertram, Torsten
中科院分区：
文献类型：
--
作者：
Kraemer, Maximilian;Roesmann, Christoph;Bertram, Torsten
协作机器人的运动规划必须适应动态环境和任务约束的变化。目前，它们检测碰撞并中断或推迟其运动计划，以防止对人类或物体造成伤害。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。在线轨迹规划器追求模型预测控制方法，以考虑任务执行期间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。最优控制问题是解决直接配置的基础上的超图结构，它代表了非线性规划，并允许有效地适应移动障碍物引起的优化问题的结构变化。为了证明该方法的有效性，机器人模仿拾取和放置任务，同时避免自碰撞，半静态和动态障碍物，包括一个人。该方法的分析涉及计算时间，约束违反，和平滑。结果表明，经过模型辨识、降阶和真实的机器人验证，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表现出最佳的折衷。模型预测控制器可以成功地接近一个移动的目标配置没有参考运动的先验知识。结果表明，纯硬约束是不够的，导致非光滑控制。结合评估障碍物接近度的软约束，规划平滑和安全的轨迹。
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.