This paper presents an optimization-based motion planning methodology for snake robots operating in constrained environments. By using a reduced-order model, the proposed approach simplifies the planning process, enabling the optimizer to autonomously generate gaits while constraining the robot's footprint within tight spaces. The method is validated through high-fidelity simulations that accurately model contact dynamics and the robot's motion. Key locomotion strategies are identified and further demonstrated through hardware experiments, including successful navigation through narrow corridors.
@article{arxiv.2503.06402,
title = {Reduced-Order Model-Based Gait Generation for Snake Robot Locomotion using NMPC},
author = {Adarsh Salagame and Eric Sihite and Milad Ramezani and Alireza Ramezani},
journal= {arXiv preprint arXiv:2503.06402},
year = {2025}
}