Automatic Snake Gait Generation Using Model Predictive Control

TL;DR

This paper introduces a model predictive control method for automatically generating adaptive snake robot gaits that optimize locomotion efficiency across various environments, including complex terrains and obstacle navigation.

Contribution

It presents a novel MPC-based approach that generates environment-adapted snake gaits without pre-defined patterns, outperforming traditional serpenoid gaits in efficiency and versatility.

Findings

Generated gaits match Pareto-optimal efficiency across environments

Method adapts to complex terrains and obstacle avoidance

Produces irregular gaits for sharp turns and obstacle navigation

Abstract

In this paper, we propose a method for generating undulatory gaits for snake robots. Instead of starting from a pre-defined movement pattern such as a serpenoid curve, we use a Model Predictive Control approach to automatically generate effective locomotion gaits via trajectory optimization. An important advantage of this approach is that the resulting gaits are automatically adapted to the environment that is being modeled as part of the snake dynamics. To illustrate this, we use a novel model for anisotropic dry friction, along with existing models for viscous friction and fluid dynamic effects such as drag and added mass. For each of these models, gaits generated without any change in the method or its parameters are as efficient as Pareto-optimal serpenoid gaits tuned individually for each environment. Furthermore, the proposed method can also produce more complex or irregular gaits, e.g. for obstacle avoidance or executing sharp turns.