Economic model predictive control for snake robot locomotion

TL;DR

This paper introduces an economic model predictive control approach for snake robot locomotion, optimizing velocity and gait selection, with proven recursive feasibility and superior performance over traditional controllers.

Contribution

It presents a novel economic MPC algorithm for snake robots that ensures recursive feasibility, integrates gait optimization, and demonstrates improved performance through simulations.

Findings

Outperforms standard lateral undulation controllers

Achieves constraint satisfaction in simulations

Identifies alternative gait patterns through optimization

Abstract

In this work, the control of snake robot locomotion via economic model predictive control (MPC) is studied. Only very few examples of applications of MPC to snake robots exist and rigorous proofs for recursive feasibility and convergence are missing. We propose an economic MPC algorithm that maximizes the robot's forward velocity and integrates the choice of the gait pattern into the closed loop. We show recursive feasibility of the MPC optimization problem, where some of the developed techniques are also applicable for the analysis of a more general class of system. Besides, we provide performance results and illustrate the achieved performance by numerical simulations. We thereby show that the economic MPC algorithm outperforms a standard lateral undulation controller and achieves constraint satisfaction. Surprisingly, a gait pattern different to lateral undulation results from the optimization.