Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks
Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks
复制标题
用于多目标机器人任务的稳定、并发控制器组合
DOI：
10.1109/cdc40024.2019.9029810
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Egerstedt
中科院分区：
文献类型：
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作者：
Anqi Li;Ching;Byron Boots;M. Egerstedt
机器人系统通常需要同时考虑多个任务。这一挑战要求控制器综合算法满足多种控制规范，同时保持整个系统的稳定性。在本文中，我们将多目标任务分解为子任务，其中各个子任务控制器被独立设计，然后组合生成总体控制策略。特别地，我们采用了黎曼运动策略（RMP），这是机器人技术中最近提出的一种控制器结构，以及RMPflow，它是用于组合RMP控制器的相关计算框架。我们通过严格的控制李雅普诺夫函数（CLF）处理，重新建立并扩展了RMPflow的稳定性结果。然后，我们证明RMPflow可以稳定地组合满足特定CLF约束的单独设计的子任务控制器。这种新的见解导致了一个有效的基于clf的计算框架，以生成同时考虑所有子任务的稳定控制器。与RMPflow的原始用法相比，我们的框架为用户提供了通过子任务的标称控制器合并设计启发式的灵活性。我们通过数值模拟和机器人实现验证了所提出的计算框架。
Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation.
使用黎曼运动策略的多机器人系统的多目标策略生成
DOI：
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发表时间：
2019
期刊：
Proceedings of the 19th International Symposium on Robotics Research
影响因子：
--
作者：
Li, A.;Mukadam, M.;Egerstedt, M.;Boots, B.
通讯作者：
Boots, B.