English

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

Robotics 2022-05-31 v1

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.

Keywords

Cite

@article{arxiv.2104.09053,
  title  = {Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61's Approach to the DARPA Subterranean Challenge},
  author = {Nicolas Hudson and Fletcher Talbot and Mark Cox and Jason Williams and Thomas Hines and Alex Pitt and Brett Wood and Dennis Frousheger and Katrina Lo Surdo and Thomas Molnar and Ryan Steindl and Matt Wildie and Inkyu Sa and Navinda Kottege and Kazys Stepanas and Emili Hernandez and Gavin Catt and William Docherty and Brendan Tidd and Benjamin Tam and Simon Murrell and Mitchell Bessell and Lauren Hanson and Lachlan Tychsen-Smith and Hajime Suzuki and Leslie Overs and Farid Kendoul and Glenn Wagner and Duncan Palmer and Peter Milani and Matthew O'Brien and Shu Jiang and Shengkang Chen and Ronald C. Arkin},
  journal= {arXiv preprint arXiv:2104.09053},
  year   = {2022}
}