集体条件反射：一种用于设计安全多机器人系统的仿生快速应急反应机制
Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction Mechanism for Designing Safe Multi-Robot Systems
摘要：
多机器人系统是一组协调的机器人，它们相互协作完成给定的任务。由于操作环境的不确定性，系统可能会遇到紧急情况，例如未观察到的障碍物，移动的车辆和极端天气。蜂群等动物群体会启动集体紧急反应行为，如绕过障碍物和避开捕食者，类似于肌肉条件反射，组织局部肌肉在第一反应中避免危险，而不会延迟通过大脑。受此启发，我们开发了一种类似的集体条件反射机制，用于多机器人系统对紧急情况的反应。本研究基于动物集体行为分析和多智能体强化学习（MARL），提出了一种基于生物启发的应急反应机制--集体条件反射（CCR）。该算法使用物理模型来确定机器人是否正在经历紧急情况;然后，对参与紧急情况的机器人的奖励增加相应的启发式奖励，该启发式奖励评估紧急情况的大小和后果，并决定本地机器人的参与。CCR在三种典型的紧急情况下进行了验证：\textit{turbulence, strong wind, and hidden obstacle}。仿真结果表明，与基线方法相比，CCR方法提高了机器人团队的应急反应能力，反应速度更快，轨迹调整更安全。
英文摘要：
A multi-robot system (MRS) is a group of coordinated robots designed to cooperate with each other and accomplish given tasks. Due to the uncertainties in operating environments, the system may encounter emergencies, such as unobserved obstacles, moving vehicles, and extreme weather. Animal groups such as bee colonies initiate collective emergency reaction behaviors such as bypassing obstacles and avoiding predators, similar to muscle-conditioned reflex which organizes local muscles to avoid hazards in the first response without delaying passage through the brain. Inspired by this, we develop a similar collective conditioned reflex mechanism for multi-robot systems to respond to emergencies. In this study, Collective Conditioned Reflex (CCR), a bio-inspired emergency reaction mechanism, is developed based on animal collective behavior analysis and multi-agent reinforcement learning (MARL). The algorithm uses a physical model to determine if the robots are experiencing an emergency; then, rewards for robots involved in the emergency are augmented with corresponding heuristic rewards, which evaluate emergency magnitudes and consequences and decide local robots' participation. CCR is validated on three typical emergency scenarios: \textit{turbulence, strong wind, and hidden obstacle}. Simulation results demonstrate that CCR improves robot teams' emergency reaction capability with faster reaction speed and safer trajectory adjustment compared with baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
