论文标题
弹性机器人团队：整合分散控制，变更检测和学习的评论
Resilient robot teams: a review integrating decentralised control, change-detection, and learning
论文作者
论文摘要
审查的目的：本文在弹性机器人团队的背景下审查了分散控制，变更检测和学习的机会和挑战。 最近的发现：外源性断层检测方法可以通过恢复解决方案提供通用检测或特定诊断。机器人团队可以执行主动和分布式感应，以检测环境中的变化，包括识别和跟踪动态异常以及协作映射动态环境。在学习感知 - 行动通信循环，多代理增强学习，体现进化，脱机演化，在线适应，明确的任务分配和群体中的污名化中，已经开发了用于分散控制的弹性方法。 摘要：弹性机器人团队的剩余挑战是整合变更检测和试验和错误学习方法，在受限的评估时间下获得可靠的绩效评估，改善了弹性机器人团队的安全性，理论上证明了对给定环境扰动的快速适应性，并设计了现实的和引人入胜的案例研究。
Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams. Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics. Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.
