mlsanityData to 7 Oct 2026

Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

arXiv 2011.01119 ↗First posted 2 Nov 2020cs.ROcited by 18

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Abstract (arXiv)

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 3; 2025: 2; 2026: 2.

18 papers.

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  1. 2022Graph Neural Networks in IoT: A Surveycited by 27
  2. 2022Graph Neural Networks for Multi-Robot Active Information Acquisitioncited by 12
  3. 2021ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecturecited by 9
  4. 2023Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offscited by 9
  5. 2022Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigationcited by 8
  6. 2023Graph learning in robotics: a surveycited by 5
  7. 2021Learning Connectivity for Data Distribution in Robot Teamscited by 3
  8. 2023Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approachcited by 3
  9. 2024LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Controlcited by 3
  10. 2023Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Networkcited by 2
  11. 2023A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loopscited by 1
  12. 2024Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertaintiescited by 1
6 more
  1. 2024Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocationcited by 1
  2. 2023Active Neural Topological Mapping for Multi-Agent Explorationcited by 0
  3. 2025Learning-aided Bigraph Matching Approach to Multi-Crew Restoration of Damaged Power Networks Coupled with Road Transportation Networkscited by 0
  4. 2025Time-Constrained Intelligent Adversaries for Automation Vulnerability Testing: A Multi-Robot Patrol Case Studycited by 0
  5. 2026A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimationcited by 0
  6. 2026Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directionscited by 0