Bioinspired Locomotion Control of Snake Robots: CPG-Based and Learning Approaches

Snake robots represent a paradigm shift in mobile robotics for navigation in unstructured environments, leveraging bioinspired control derived from natural neural mechanisms. This review synthesizes research on snake robot locomotion control, focusing on two dominant methodologies. Central Pattern Generator (CPG) based control and learning-based approaches. We examine Matsuoka and Hopf oscillators, parameter optimization via evolutionary algorithms, and hybrid CPG-learning architectures. Learning approaches encompass reinforcement learning, evolutionary optimization, and spiking neural networks. We synthesize insights from peer-reviewed literature covering rigid and soft platforms, evaluate comparative performance across terrains, and identify critical gaps in sim-to-real transfer, energy efficiency, and sensory integration. This review guides researchers toward practical deployment of adaptive snake robot systems.