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Dynamic task allocation approaches for coordinated exploration of Subterranean environments

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Abstract

This paper presents the methods used by team CSIRO Data61 for multi-agent coordination and exploration in the DARPA Subterranean (SubT) Challenge. The SubT competition involved a single operator sending teams of robots to rapidly explore underground environments with severe navigation and communication challenges. Coordination was framed as a multi-robot task allocation (MRTA) problem to allow for a seamless integration of exploration with other required tasks. Methods for extending a consensus-based task allocation approach for an online and highly dynamic mission are discussed. Exploration tasks were generated from frontiers in a map of traversable space, and graph-based heuristics applied to guide the selection of exploration tasks. Results from simulation, field testing, and the final competition are presented. Team CSIRO Data61 tied for most points scored and achieved second place during the final SubT event.

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References

  • Arkin, R. C., & Diaz, J. (2002). Line-of-sight constrained exploration for reactive multiagent robotic teams. In 7th International workshop on advanced motion control. proceedings (Cat. No. 02TH8623) (pp. 455–461). IEEE.

  • Atay, N., & Bayazit, B. (2006). Mixed-integer linear programming solution to multi-robot task allocation problem.

  • Bourgault, F., Makarenko, A. A., Williams, S. B., Grocholsky, B., & Durrant-Whyte, H. F. (2002). Information based adaptive robotic exploration. In IEEE/RSJ international conference on intelligent robots and systems1 (pp. 540–545). IEEE.

  • Brass, P., Cabrera-Mora, F., Gasparri, A., & Xiao, J. (2011). Multirobot tree and graph exploration. IEEE Transactions on Robotics, 27(4), 707–717.

    Article  Google Scholar 

  • Buckman, N., Choi, H.-L., & How, J. P. (2019). Partial replanning for decentralized dynamic task allocation. In AIAA Scitech 2019 Forum (p. 0915).

  • Burgard, W., Moors, M., Stachniss, C., & Schneider, F. E. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386.

    Article  Google Scholar 

  • Cepeda, J. S., Chaimowicz, L., Soto, R., Gordillo, J. L., Alanís-Reyes, E. A., & Carrillo-Arce, L. C. (2012). A behavior-based strategy for single and multi-robot autonomous exploration. Sensors, 12(9), 12772–12797.

    Article  Google Scholar 

  • Chen, S., O’Brien, M. J., Talbot, F., Williams, J. L., Tidd, B., Pitt, A., & Arkin, R. C. (2022). Multimodal user interface for multi-robot control in underground environments. In submission.

  • Chen, S., O’Brien, M. J., Talbot, F., Williams, J. L., Tidd, B., Pitt, A., Arkin, R. C. (2022). Multimodal user interface for multi-robot control in under-ground environments. In: IEEE International Conference on Intelligent Robots And Systems

  • Choi, H.-L., Brunet, L., & How, J. P. (2009). Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics, 25(4), 912–926.

    Article  Google Scholar 

  • Chung, T. (2019). DARPA Subterranean (SubT) Challenge. https://www.darpa.mil/program/darpa-subterranean-challenge

  • Dai, W., Lu, H., Xiao, J., Zeng, Z., & Zheng, Z. (2020). Multi-robot dynamic task allocation for exploration and destruction. Journal of Intelligent & Robotic Systems, 98(2), 455–479.

    Article  Google Scholar 

  • Dang, T., Mascarich, F., Khattak, S., Papachristos, C., & Alexis, K. (2019). Graph-based path planning for autonomous robotic exploration in subterranean environments. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3105–3112). IEEE.

  • Faigl, J., & Kulich, M. (2015). On benchmarking of frontier-based multi-robot exploration strategies. In 2015 European conference on mobile robots (ECMR) (pp. 1–8). IEEE.

  • Fraigniaud, P., Gasieniec, L., Kowalski, D. R., & Pelc, A. (2006). Collective tree exploration. Networks: An International Journal, 48(3), 166–177.

    Article  MathSciNet  MATH  Google Scholar 

  • Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939–954.

    Article  Google Scholar 

  • Goodman, J., Greenberg, A. G., Madras, N., & March, P. (1988). Stability of binary exponential backoff. Journal of the ACM (JACM), 35(3), 579–602.

    Article  MathSciNet  Google Scholar 

  • Hudson, N., Talbot, F., Cox, M., Williams, J., Hines, T., Pitt, A., Wood, B., Frousheger, D., Lo Surdo, K., Molnar, T., Steindl, R., Wildie, M., Sa, I., Kottege, N., Stepanas, K., Hernandez, E., Catt, G., Docherty, W., Tidd, B., & Arkin, R. C. (2022). Heterogeneous ground and air platforms, homogeneous sensing: Team CSIRO Data61’s approach to the DARPA Subterranean Challenge. Field Robotics, 2, 595–636. https://doi.org/10.55417/fr.2022021

  • Jain, U., Tiwari, R., & Godfrey, W. W. (2017). Comparative study of frontier based exploration methods. In 2017 Conference on information and communication technology (CICT) (pp. 1–5). IEEE.

  • Johnson, L., Ponda, S., Choi, H.-L., & How, J. (2011). Asynchronous decentralized task allocation for dynamic environments. In Infotech@ Aerospace 2011 (p. 1441).

  • Keidar, M., & Kaminka, G. A. (2014). Efficient frontier detection for robot exploration. The International Journal of Robotics Research, 33(2), 215–236. https://doi.org/10.1177/0278364913494911

    Article  Google Scholar 

  • Kottege, N., Williams, J., & Tidd, B., Talbot, F., Steindl, R., Cox, M., Frousheger, D., Hines, T., Pitt, A., Tam, B., Wood, B., Hanson, L., Lo Surdo, K., Molnar, T., Wildie, M., Stepanas, K., Catt, G., Tychsen-Smith, L., Penfold, D., Arkin, R. (2023. Heterogeneous robot teams with unified perception and autonomy: How Team CSIRO Data61 tied for the top score at the DARPA Subterranean Challenge, Accepted for publication in Field Robotics. arXiv:2302.13230

  • Khamis, A., Hussein, A., & Elmogy, A. (2015). Multi-robot task allocation: A review of the state-of-the-art. Cooperative Robots and Sensor Networks, 2015, 31–51.

    Google Scholar 

  • Kim, S.-K., Bouman, A., Salhotra, G., Fan, D. D., Otsu, K., Burdick, J., & Agha-mohammadi, A.-A. (2021) Plgrim: Hierarchical value learning for large-scale exploration in unknown environments. In Proceedings of the international conference on automated planning and scheduling, 31 (pp. 652–662).

  • Mosteo, A. R., & Montano, L. (2006). Simulated annealing for multi-robot hierarchical task allocation with flexible constraints and objective functions. In Workshop on network robot systems toward intelligent robotic systems integrated with environments, Citeseer.

  • Nieto-Granda, C., Rogers, J. G., III., & Christensen, H. I. (2014). Coordination strategies for multi-robot exploration and mapping. The International Journal of Robotics Research, 33(4), 519–533.

    Article  Google Scholar 

  • Olson, E., Strom, J., Morton, R., Richardson, A., Ranganathan, P., Goeddel, R., Bulic, M., Crossman, J., & Marinier, B. (2012). Progress toward multi-robot reconnaissance and the magic 2010 competition. Journal of Field Robotics, 29(5), 762–792.

    Article  Google Scholar 

  • Saha, P. K., Borgefors, G., & di Baja, G. S. (2016). A survey on skeletonization algorithms and their applications. Pattern Recognition Letters, 76, 3–12.

    Article  Google Scholar 

  • Simmons, R., Apfelbaum, D., Burgard, W., Fox, D., Moors, M., Thrun, S., & Younes, H. (2000). Coordination for multi-robot exploration and mapping. In AAAI/IAAI (pp. 852–858).

  • Williams, J., Jiang, S., O’Brien, M., Wagner, G., Hernandez, E., Cox, M., Pitt, A., Arkin, R., & Hudson, N. (2020). Online 3D frontier-based UGV and UAV exploration using direct point cloud visibility. In 2020 IEEE International conference on multisensor fusion and integration for intelligent systems (MFI) (pp. 263–270). IEEE.

  • Yamauchi, B. (1997). A frontier-based approach for autonomous exploration. In CIRA (pp. 146–151).

  • Yang, K., Keat Gan, S., & Sukkarieh, S. (2013). A gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV. Advanced Robotics, 27(6), 431–443.

    Article  Google Scholar 

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Funding

This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

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Authors and Affiliations

  1. Mobile Robot Lab, Georgia Institute of Technology, Atlanta, GA, USA

    Matthew O’Brien, Shengkang Chen & Ronald Arkin

  2. Robotics and Autonomous Systems Group, CSIRO Data61, Pullenvale, QLD, Australia

    Jason Williams, Alex Pitt & Navinda Kottege

Authors
  1. Matthew O’Brien
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  2. Jason Williams
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  3. Shengkang Chen
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  4. Alex Pitt
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  5. Ronald Arkin
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  6. Navinda Kottege
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Contributions

M.O. was the lead author. M.O., J.W. and R.A. contributed to the system design. M.O., J.W., S.C. and A.P. contributed to system implementation, experiment and analysis, and R.A. and N.K. provided guidance and oversight. The authors also thank Fletcher Talbot, Brendan Tidd, Brett Wood and Tom Hines for assistance with the field experiment.

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Correspondence to Matthew O’Brien.

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O’Brien, M., Williams, J., Chen, S. et al. Dynamic task allocation approaches for coordinated exploration of Subterranean environments. Auton Robot 47, 1559–1577 (2023). https://doi.org/10.1007/s10514-023-10142-4

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