Design and Optimization of Gait Transition Control for Loaded Snake Robots Using CPG Algorithms

Inspired by the exceptional locomotive efficiency and adaptability of snakes across diverse terrains, this study employs a Central Pattern Generator (CPG) algorithm based on the Matsuoka oscillator model. Through fine-tuning of the CPG control parameters, the developed snake robot is capable of generating not only rhythmic undulatory motions but also a spectrum of distinct gaits, including rectilinear progression and accordion-like maneuvers. Leveraging the stability of the Matsuoka oscillator, the CPG endows the robot with the capacity to dynamically adapt to varying environmental conditions and mission-specific demands. The primary control parameters are optimized using genetic algorithms, facilitating seamless gait transitions. This research pioneers the application of this method to gait transformation in snake robots, with experimental validation, thereby holding significant implications for advancements in the field.