An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
Ali Agha, Kyohei Otsu, Benjamin Morrell, David D. Fan, Sung-Kyun Kim, Muhammad Fadhil Ginting, Xianmei Lei, Jeffrey Edlund, Seyed Fakoorian, Amanda Bouman, Fernando Chavez, Taeyeon Kim, Gustavo J. Correa, Maira Saboia, Angel Santamaria-Navarro, Brett Lopez, Boseong Kim, Chanyoung Jung, Mamoru Sobue, Oriana Claudia Peltzer, Joshua Ott, Robert Trybula, Thomas Touma, Marcel Kaufmann, Tiago Stegun Vaquero, Torkom Pailevanian, Matteo Palieri, Yun Chang, Andrzej Reinke, Matthew Anderson, Frederik E. T. Schöller, Patrick Spieler, Lillian M. Clark, Avak Archanian, Kenny Chen, Hovhannes Melikyan, Anushri Dixit, Harrison Delecki, Daniel Pastor, Barry Ridge, Nicolas Marchal, Jose Uribe, Sharmita Dey, Kamak Ebadi, Kyle Coble, Alexander Nikitas Dimopoulos, Vivek Thangavelu, Vivek S. Varadharajan, Nicholas Palomo, Antoni Rosinol, Arghya Chatterjee, Christoforos Kanellakis, Bjorn Lindqvist, Micah Corah, Kyle Strickland, Ryan Stonebraker, Michael Milano, Christopher E. Denniston, Sami Sahnoune, Thomas Claudet, Seungwook Lee, Gautam Salhotra, Edward Terry, Rithvik Musuku, Robin Schmid, Tony Tran, Ara Kourchians, Justin Schachter, Hector Azpurua, Levi Resende, Arash Kalantari, Jeremy Nash, Josh Lee, Christopher Patterson, Jennifer G. Blank, Kartik Patath, Yuki Kubo, Ryan Alimo, Yasin Almalioglu, Aaron Curtis, Jacqueline Sly, Tesla Wells, Nhut T. Ho, Mykel Kochenderfer, Giovanni Beltrame, George Nikolakopoulos, David Shim, Luca Carlone, Joel Burdick
TL;DR
This work extends NeBula with scalable, multi-robot autonomy for large-scale subterranean environments by enhancing state estimation (LOCUS 2.0, AMCCKF/HeRO), large-scale 3D mapping (LAMP 2.0), semantic understanding (EaRLaP), risk-aware traversability (STEP), uncertainty-aware global planning (PLGRIM), and robust multi-robot networking (ACHORD). It also integrates drone hardware and autonomy (Aquila/Proxima) with ground platforms, enabling ground-aerial collaboration and comms-aware mission planning. Field and SubT Final Event results demonstrate improved localization accuracy, map quality, artifact detection, and mission throughput across diverse environments, validating the approach for real-world, large-scale subterranean exploration. Overall, NeBula 2.0 delivers a cohesive, scalable framework for autonomous multi-robot exploration in challenging unknown environments, with proven deployments in limestone mines and DARPA Subterranean Challenge finals.
Abstract
This paper presents an appendix to the original NeBula autonomy solution developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), participating in the DARPA Subterranean Challenge. Specifically, this paper presents extensions to NeBula's hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: (i) large-scale geometric and semantic environment mapping; (ii) an adaptive positioning system; (iii) probabilistic traversability analysis and local planning; (iv) large-scale POMDP-based global motion planning and exploration behavior; (v) large-scale networking and decentralized reasoning; (vi) communication-aware mission planning; and (vii) multi-modal ground-aerial exploration solutions. We demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.