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Large-Scale Heterogeneous Multi-robot Coverage via Domain Decomposition and Generative Allocation

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Algorithmic Foundations of Robotics XV (WAFR 2022)

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Abstract

This paper develops a new approach to direct a set of heterogeneous agents, varying in mobility and sensing capabilities, to quickly cover a large region, say for example in the search for victims after a large-scale disaster. Given that time is of the essence, we seek to mitigate computational complexity, which normally grows exponentially as the number of agents increases. We create a new framework which reduces the planning complexity through simultaneously decomposing a target domain into sub-regions, and assigning a team of agents to each sub-region in the target domain, as a way to decompose a large-scale problem into a set of smaller problems. The teams are formed to optimize the coverage of each sub-regions. Doing so requires both the utilization of individual agents’ strengths as well as their collaborative capabilities. We determine the ideal team by introducing a novel evolution-guided generative model based on generative adversarial networks (GANs) that creates allocation plans from the sub-region features in a computationally efficient manner. We validate our framework on a real-world satellite images dataset, and demonstrate that through decomposition and generative allocation, our method has significantly better efficiency and efficacy compared to current centralized multi-robot coverage methods, and is therefore better suited for large-scale time-critical deployment.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 15289, USA

    Jiaheng Hu, Howard Coffin, Julian Whitman, Matthew Travers & Howie Choset

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  1. Jiaheng Hu
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  2. Howard Coffin
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  3. Julian Whitman
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  4. Matthew Travers
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  5. Howie Choset
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Corresponding author

Correspondence to Jiaheng Hu .

Editor information

Editors and Affiliations

  1. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Steven M. LaValle

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Jason M. O’Kane

  3. Department of Aerospace Engineering, University of Maryland, College Park, MD, USA

    Michael Otte

  4. Gates Computer Science, Stanford University, Stanford, CA, USA

    Dorsa Sadigh

  5. Department of Computer Science, University of Maryland, College Park, MD, USA

    Pratap Tokekar

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Hu, J., Coffin, H., Whitman, J., Travers, M., Choset, H. (2023). Large-Scale Heterogeneous Multi-robot Coverage via Domain Decomposition and Generative Allocation. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_4

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