Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction Mechanism for Designing Safe Multi-Robot Systems

TL;DR

This paper introduces a bio-inspired collective conditioned reflex mechanism for multi-robot systems, enabling faster and safer emergency responses by mimicking animal group behaviors and using reinforcement learning.

Contribution

It develops a novel emergency reaction mechanism based on animal collective behavior and multi-agent reinforcement learning, enhancing multi-robot systems' safety and responsiveness.

Findings

CCR improves emergency reaction speed

CCR results in safer trajectory adjustments

Validated on turbulence, wind, and obstacle scenarios

Abstract

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.