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A Decentralized Framework for Multi-Agent Robotic Systems.

Andrés C Jiménez1, Vicente García-Díaz2, Sandro Bolaños3

  • 1Department of Electronic Engineering, Los Libertadores Foundation University, Cr.16#63A-68 Bogotá, Colombia. acjimeneza@libertadores.edu.co.

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This study introduces a novel communication framework for decentralized multi-agent robotic systems, enhancing robustness by allowing agents to join or leave networks dynamically. The system uses signal strength and data history for efficient information transfer, improving task completion in dynamic environments.

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communicationdecentralizationdistributed systemsmulti-agent robotic systems

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Area of Science:

  • Robotics
  • Distributed Systems
  • Network Communication

Background:

  • Decentralized multi-agent robotic systems offer robust task execution without central control.
  • Existing systems often overlook network communication, creating dependencies on permanent links between agents.
  • This limitation hinders flexibility and resilience in dynamic environments.

Purpose of the Study:

  • To propose a flexible and robust communication framework for decentralized multi-agent robotic systems.
  • To address the limitations of permanent network links in agent-based systems.
  • To enable agents to dynamically join, leave, and communicate within the network.

Main Methods:

  • Developed a communication framework where agents manage their network participation dynamically.
  • Implemented four core processes for agent participation and a fifth for data transfer.
  • Utilized Received Signal Strength Indicator (RSSI) and data transfer history for node proximity and information dissemination.
  • Employed differential robotic agents and a monitoring agent for framework validation.

Main Results:

  • Demonstrated a communication framework enabling agents to join/leave networks adaptively.
  • Successfully utilized RSSI and data history for efficient, proximity-based data transfer.
  • Validated the framework's effectiveness in generating a topological map of an environment with obstacles.
  • Showcased improved communication robustness compared to systems requiring permanent links.

Conclusions:

  • The proposed communication framework enhances the robustness and flexibility of decentralized multi-agent robotic systems.
  • Dynamic network participation and history-based data transfer are key to overcoming communication dependencies.
  • The framework provides a viable solution for agent communication in complex and changing environments.
  • This research contributes to more resilient and adaptable robotic systems.