Enabling Autonomous Navigation in a Snake Robot through Visual-Inertial Odometry and Closed-Loop Trajectory Tracking Control

TL;DR

This paper presents an autonomous navigation system for a snake robot using visual-inertial SLAM, reduced-order state estimation, and closed-loop control, enabling precise waypoint tracking in challenging terrains.

Contribution

It introduces a complete autonomy pipeline for snake robots, integrating onboard perception, state estimation, and control for the first time in this context.

Findings

Accurate multi-waypoint tracking demonstrated in physical experiments.

Real-time localization validated against motion-capture data.

Identified drift behavior and failure modes specific to snake robots.

Abstract

Snake robots offer exceptional mobility across extreme terrain inaccessible to conventional rovers, yet their highly articulated bodies present fundamental challenges for autonomous navigation in environments lacking external tracking infrastructure. This thesis develops a complete autonomy pipeline for COBRA, an 11 degree-of-freedom modular snake robot designed for planetary exploration. While the robot's biologically inspired serpentine gaits achieve impressive mobility, prior work has relied entirely on open-loop teleoperation. This approach integrates onboard visual-inertial SLAM, reduced-order state estimation, and closed-loop trajectory tracking to enable autonomous waypoint navigation. A depth camera paired with edge computing performs real-time localization during dynamic locomotion, validated against motion-capture ground truth to characterize drift behavior and failure modes unique to snake robot platforms. A reduced-order framework estimates Center-of-Mass pose, driving a closed-loop controller that modulates CPG gait parameters through distance-dependent yaw error blending. Physical experiments validate the complete system, demonstrating accurate multi-waypoint tracking and establishing foundations for autonomous snake robot navigation.