Snake Robot Gait Decomposition and Gait Parameter Optimization

TL;DR

This paper introduces Gait Decomposition and Gait Parameter Gradient methods for mathematically analyzing and optimizing snake robot gaits, enabling flexible gait generation and parameter tuning.

Contribution

It presents novel mathematical decomposition and optimization techniques for snake robot gaits, simplifying parameter tuning and expanding practical applications.

Findings

G.D allows concise mathematical expression of snake gaits.

GPG effectively optimizes gait parameters and functions.

The methods facilitate diverse gait generation with fewer parameters.

Abstract

This paper proposes Gait Decomposition (G.D), a method of mathematically decomposing snake movements, and Gait Parameter Gradient (GPG), a method of optimizing decomposed gait parameters. G.D is a method that can express the snake gait mathematically and concisely from generating movement using the curve function to the motor control order when generating movement of snake robot. Through this method, the gait of the snake robot can be intuitively classified into a matrix, as well as flexibly adjusting the parameters of the curve function required for gait generation. This can solve the problem that parameter tuning, which is the reason why it is difficult for a snake robot to practical use, is difficult. Therefore, if this G.D is applied to snake robots, various gaits can be generated with a few of parameters, so snake robots can be used in many fields. We also implemented the GPG algorithm to optimize the gait curve function as well as define the gait of the snake robot through G.D.