Flexible Supervised Autonomy for Exploration in Subterranean Environments

TL;DR

This paper presents a flexible autonomous exploration system for subterranean environments, combining independent single-agent autonomy with mission management over mesh networks, demonstrated in DARPA's SubT Challenge.

Contribution

It introduces a novel autonomous system architecture that enables long-term, minimally supervised exploration in complex underground environments.

Findings

System successfully explored complex underground environments.

Demonstrated vehicle versatility and system adaptability.

Achieved effective autonomous operation with minimal human supervision.

Abstract

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.