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Information Distribution in Multi-Robot Systems: Generic, Utility-Aware Optimization Middleware.

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Summary
This summary is machine-generated.

This study optimizes multi-robot communication by determining essential information transfer under constraints. Monte Carlo Tree Search efficiently manages data flow, improving system performance and enabling real-world applications.

Keywords:
Monte Carlo tree searchadaptive communicationcommunication optimizationinformation distributioninformation utilitymulti-robot systems

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Multi-robot systems require efficient communication strategies.
  • Limited resources like bandwidth and energy pose significant challenges.
  • Optimizing information sharing is crucial for coordinated robotic operations.

Purpose of the Study:

  • To develop a method for deciding what information is most valuable to send in multi-robot systems.
  • To address constraints such as limited throughput and energy.
  • To create a transparent middleware for enhancing existing communication systems.

Main Methods:

  • Utilized Monte Carlo Tree Search (MCTS) for decision-making.
  • Introduced techniques to reduce the decision space for improved efficiency.
  • Implemented and evaluated the approach in a simulation study.
  • Demonstrated feasibility through a proof-of-concept in ROS 2 on mobile robots.

Main Results:

  • The proposed method effectively determines critical information for multi-robot communication.
  • Techniques for reducing the decision space significantly enhanced performance.
  • Successful validation in both simulation and a real-world ROS 2 environment.

Conclusions:

  • The Monte Carlo Tree Search-based approach provides an effective solution for optimizing communication in constrained multi-robot systems.
  • The developed middleware is adaptable and can be integrated into existing robotic frameworks.
  • The study confirms the practical applicability of the method in real-world robotic deployments.