Obstacle Avoidance Using Population Vector Code-Based SNN-CPG Controller for Robotic Fish in Unknown Environment

Neurorobotics, which incorporates neuromorphic computing into robotic systems, can emulate biological intelligence and behaviors. The characteristics of neural responses and behavioral patterns are reproduced using spiking neural networks. Many applications utilize mobility robots; however, applications with multiple joints, such as robotic fish, remain limited. The main challenge is that shape types and joint numbers vary among biological organisms, and disturbances affect them in underwater environments. The neural circuit mechanisms used by multi-jointed robots for obstacle avoidance have not yet been clarified. In this study, we propose an obstacle avoidance architecture realized exclusively through neural circuits. The fundamental obstacle avoidance performance of a robotic fish was systematically evaluated, including ablation studies, varying conditions, and the physical environment.