Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61's Approach to the DARPA Subterranean Challenge

TL;DR

Team CSIRO Data61 developed a heterogeneous robot team with homogeneous sensing capabilities for subterranean exploration, enabling decentralized multi-agent SLAM and autonomous operation in challenging environments during the DARPA Subterranean Challenge.

Contribution

The paper introduces a multi-agent system with homogeneous sensing for heterogeneous robots, emphasizing decentralized SLAM and autonomy in subterranean environments.

Findings

Homogeneous sensing enables effective decentralized SLAM.

Rugged tracked platforms perform well in challenging terrains.

Heterogeneous team structure improves exploration capabilities.

Abstract

Heterogeneous teams of robots, leveraging a balance between autonomy and human interaction, bring powerful capabilities to the problem of exploring dangerous, unstructured subterranean environments. Here we describe the solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent and Georgia Tech, during the DARPA Subterranean Challenge. These presented systems were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in February 2020, and in our own Cave event, conducted in September 2020. A unique capability of the fielded team is the homogeneous sensing of the platforms utilised, which is leveraged to obtain a decentralised multi-agent SLAM solution on each platform (both ground agents and UAVs) using peer-to-peer communications. This enabled a shift in focus from constructing a pervasive communications network to relying on multi-agent autonomy, motivated by experiences in early circuit events. These experiences also showed the surprising capability of rugged tracked platforms for challenging terrain, which in turn led to the heterogeneous team structure based on a BIA5 OzBot Titan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked or legged ground robots. The ground agents use a common CatPack perception module, which allowed reuse of the perception and autonomy stack across all ground agents with minimal adaptation.