Machine Learning-Driven Burrowing with a Snake-Like Robot

TL;DR

This paper presents a machine learning control strategy for a snake-like robot to improve subterranean burrowing efficiency, outperforming standard techniques by automatically reaching targeted depths using magnetic field data.

Contribution

Introduces a novel deep learning architecture utilizing magnetic field data for optimal vertical self-burrowing in a bio-inspired snake robot.

Findings

Outperforms standard burrowing methods

Automatically reaches targeted depths

Effective use of magnetic field as depth proxy

Abstract

Subterranean burrowing is inherently difficult for robots because of the high forces experienced as well as the high amount of uncertainty in this domain. Because of the difficulty in modeling forces in granular media, we propose the use of a novel machine-learning control strategy to obtain optimal techniques for vertical self-burrowing. In this paper, we realize a snake-like bio-inspired robot that is equipped with an IMU and two triple-axis magnetometers. Utilizing magnetic field strength as an analog for depth, a novel deep learning architecture was proposed based on sinusoidal and random data in order to obtain a more efficient strategy for vertical self-burrowing. This strategy was able to outperform many other standard burrowing techniques and was able to automatically reach targeted burrowing depths. We hope these results will serve as a proof of concept for how optimization can be used to unlock the secrets of navigating in the subterranean world more efficiently.