  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.
