Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

TL;DR

This paper presents advanced multi-agent autonomous exploration algorithms for subterranean environments, addressing communication, navigation, and coordination challenges to improve robotic search capabilities in complex underground scenarios.

Contribution

It introduces two novel navigation algorithms and a multi-agent coordination strategy with communication beacons, enhancing autonomous subterranean exploration.

Findings

Effective multi-agent exploration in complex environments

Communication beacons extend information sharing range

Field testing is crucial for system refinement

Abstract

Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous exploration of unknown subterranean environments in which coordinated agents search for artifacts of interest. The team presents two navigation algorithms in the form of a metric-topological graph-based planner and a continuous frontier-based planner. To facilitate multi-agent coordination, agents share and merge new map information and candidate goal-points. Agents deploy communication beacons at different points in the environment, extending the range at which maps and other information can be shared. Onboard autonomy reduces the load on human supervisors, allowing agents to detect and localize artifacts and explore autonomously outside established communication networks. Given the scale, complexity, and tempo of this challenge, a range of lessons were learned, most importantly, that frequent and comprehensive field testing in representative environments is key to rapidly refining system performance.