[论文解读] Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems

Amanda Prorok, Matthew Malencia+4 more|arXiv (Cornell University)|Sep 25, 2021

Reinforcement Learning in Robotics参考文献 268被引用 40

一句话总结

本文在多机器人系统中定义韧性，区分其与鲁棒性，并提出在感知、规划与控制领域设计具有韧性的机器人网络的正式分类法和框架。

ABSTRACT

Robustness is key to engineering, automation, and science as a whole. However, the property of robustness is often underpinned by costly requirements such as over-provisioning, known uncertainty and predictive models, and known adversaries. These conditions are idealistic, and often not satisfiable. Resilience on the other hand is the capability to endure unexpected disruptions, to recover swiftly from negative events, and bounce back to normality. In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori. As society increasingly depends on connected automated systems to provide key infrastructure services (e.g., logistics, transport, and precision agriculture), providing the means to achieving resilient multi-robot systems is paramount. By enumerating the consequences of a system that is not resilient (fragile), we argue that resilience must become a central engineering design consideration. Towards this goal, the community needs to gain clarity on how it is defined, measured, and maintained. We address these questions across foundational robotics domains, spanning perception, control, planning, and learning. One of our key contributions is a formal taxonomy of approaches, which also helps us discuss the defining factors and stressors for a resilient system. Finally, this survey article gives insight as to how resilience may be achieved. Importantly, we highlight open problems that remain to be tackled in order to reap the benefits of resilient robotic systems.

研究动机与目标

• 澄清并形式化韧性作为多机器人网络中的系统级能力。
• 将韧性与传统鲁棒性以及过度供给区分开来。
• 提供机器人学韧性工程的正式模型与术语。
• 提出韧性方法的分类法并将其与应激源类型联系起来。
• 识别具有韧性的机器人系统的开放问题与未来方向。

提出的方法

• 通过正式定义与示意图界定韧性并将其与鲁棒性进行对比。
• 引入应激源的分类法（随机性与分布外；定向与非定向）以及方法类型（事前、操作中、操作后）。
• 建立用于在感知、规划和控制领域对应激源和韧性目标进行建模的数学符号。
• 使用所提出的分类法对现有工作进行分类并概述未解决的问题。
• 讨论三个应用领域（感知、规划、控制）以及韧性在每个领域中的表现。

实验结果

研究问题

• RQ1 在多机器人系统中韧性由何定义，及其与鲁棒性有何不同？
• RQ2 韧性如何在联网机器人系统中建模、测量和工程实现？
• RQ3 哪种应激源与方法类型的分类最能体现机器人领域的韧性策略？
• RQ4 韧性概念如何在多机器人系统的感知、规划和控制领域中应用？
• RQ5 还有哪些开放问题需要解决以推动具有韧性的多机器人工程？

主要发现

• 通过利用系统级的互补性、多样性和冗余来维持功能或在中断时将停机时间降至最低，从而实现韧性。
• 正式分类法区分应激源（随机性与分布外）及韧性工程的方法类型（事前、事中、事后）。
• 本文引入了一个正式模型与记号，以支持韧性设计与分析。
• 它提供了一个关于感知、规划与控制领域韧性的结构化综述，并指出未来工作的开放问题。