Graph Neural Network for Decentralized Multi-Robot Goal Assignment

Manohari Goarin, Giuseppe Loianno

Research output: Contribution to journalArticlepeer-review


The problem of assigning a set of spatial goals to a team of robots plays a crucial role in various multi-robot planning applications including, but not limited to exploration, search and rescue, or surveillance. The Linear Sum Assignment Problem (LSAP) is a common way of formulating and resolving this problem. This optimization problem aims at assigning the tasks to the robots minimizing the sum of costs while respecting a one-to-one matching constraint. However, communication restrictions in real-world scenarios pose significant challenges. Existing decentralized solutions often rely on numerous communication interactions to converge to a conflict-free and optimal solution or assume a prior conflict-free random assignment. In this paper, we propose a novel Decentralized Graph Neural Network approach for multi-robot Goal Assignment (DGNN-GA). We leverage a heterogeneous graph representation to model the inter-robot communication and the assignment relations between goals and robots. We compare in simulation its performance to other decentralized state-of-the-art approaches. Specifically, our method outperforms popular state-of-the art approaches in strictly restricted communication scenarios and does not rely on any initial conflict-free guess compared to two other algorithms. Finally, the DGNN-GA is also deployed and validated in real-world experiments.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number5
StateAccepted/In press - 2024


  • Costs
  • Deep Learning Methods
  • Graph neural networks
  • Integrated Planning and Learning
  • Multi-robot systems
  • Planning
  • Prediction algorithms
  • Robots
  • Task analysis
  • Task and Motion Planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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