Optimizing Snake Robot Locomotion with Decomposed Gait Pattern Representation

Abstract

This paper presents novel Gait Decomposition (GD) and Gait Parameter Gradient (GPG) methods for enhancing snake robot control and optimization. Snake robots face challenges in parameter tuning due to their complex dynamics and the need to preserve gait characteristics during control. GD fine-tunes gait parameters while maintaining their characteristics to prevent unintended changes during the application of serpenoid curves, typical in snake robots. A key feature of GD is the use of a motion matrix to represent joint movements, ensuring the preservation of gait characteristics. This methodology classifies the robot’s gait as a motion matrix, aiding in addressing the common challenge of parameter tuning in real-world scenarios. Furthermore, we introduce the GPG algorithm, designed to efficiently optimize gait parameters by adjusting both the curve function parameters and the motion matrix. Simulations validate the effectiveness of our methods, showing that the decomposed gait closely retains the original gait’s characteristics and achieves stable optimization under various conditions. Together, GD and GPG offer significant improvements in the control, adaptability, and practical deployment of snake robots, potentially expanding their applications across various domains.