A Novel Fish-inspired Self-adaptive Approach to Collective Escape of Swarm Robots Based on Neurodynamic Models

Fish schools present high-efficiency group behaviors to collective migration and dynamic escape from the predator through simple individual interactions. The purpose of this research is to infuse swarm robots with "fish-like" intelligence that will enable safe navigation and efficient cooperation, and successful completion of escape tasks in changing environments. In this paper, a novel fish-inspired self-adaptive approach is proposed for the collective escape of swarm robots. A bio-inspired neural network (BINN) is introduced to generate collision-free escape trajectories through the dynamics of neural activity and the combination of attractive and repulsive forces. In addition, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in dynamic environments. Similar to fish escape maneuvers, simulations and real-robot experiments show that the swarm robots can collectively leave away from the threat and respond to sudden environmental changes. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness, efficiency, and flexibility of swarm robots in complex environments.