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

Authors :
Hudson, Nicolas
Talbot, Fletcher
Cox, Mark
Williams, Jason
Hines, Thomas
Pitt, Alex
Wood, Brett
Frousheger, Dennis
Surdo, Katrina Lo
Molnar, Thomas
Steindl, Ryan
Wildie, Matt
Sa, Inkyu
Kottege, Navinda
Stepanas, Kazys
Hernandez, Emili
Catt, Gavin
Docherty, William
Tidd, Brendan
Tam, Benjamin
Murrell, Simon
Bessell, Mitchell
Hanson, Lauren
Tychsen-Smith, Lachlan
Suzuki, Hajime
Overs, Leslie
Kendoul, Farid
Wagner, Glenn
Palmer, Duncan
Milani, Peter
O'Brien, Matthew
Jiang, Shu
Chen, Shengkang
Arkin, Ronald C.
Source :
Field Robotics vol. 2, 2022
Publication Year :
2021

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.

Subjects

Subjects :
Computer Science - Robotics

Details

Database :
arXiv
Journal :
Field Robotics vol. 2, 2022
Publication Type :
Report
Accession number :
edsarx.2104.09053
Document Type :
Working Paper
Full Text :
https://doi.org/10.55417/fr.2022021