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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Multiple Leader Candidate and Competitive Position Allocation for Robust Formation against Member Robot Faults.

Ji-Wook Kwon1, Jin Hyo Kim2,3, Jiwon Seo4,5

  • 1Yonsei Institute of Convergence Technology, Yonsei University, 85 Songdogwahak-ro, Incheon 406-840, Korea. bluemichael@yonsei.ac.kr.

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Summary

This study introduces a Multiple Leader Candidate (MLC) structure and Competitive Position Allocation (CPA) algorithm for robust, cost-effective multi-robot systems. This approach enhances fault tolerance and system performance in applications like environmental sensing.

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Area of Science:

  • Robotics
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Traditional multi-robot formation structures often lack robustness against member robot failures.
  • Existing position allocation methods can be rigid or computationally intensive.
  • There is a need for adaptable and resilient multi-robot coordination strategies.

Purpose of the Study:

  • To propose a novel Multiple Leader Candidate (MLC) structure for decentralized robot systems.
  • To introduce a Competitive Position Allocation (CPA) algorithm for robust formation control.
  • To demonstrate the applicability and performance of the MLC structure and CPA algorithm in multi-robot systems, including environmental sensing.

Main Methods:

  • Development of a Multiple Leader Candidate (MLC) structure where a system leader is dynamically chosen.
  • Implementation of a decentralized Competitive Position Allocation (CPA) algorithm for robot position assignment through inter-robot competition.
  • Validation through numerical simulations and experimental results.

Main Results:

  • The proposed MLC structure and CPA algorithm demonstrate robustness against the disappearance of member robots.
  • The system achieves reduced overall cost compared to existing formation control methods.
  • Feasibility and performance of the proposed approach are confirmed via simulations and experiments.

Conclusions:

  • The MLC structure and CPA algorithm offer a resilient and cost-effective solution for multi-robot formations.
  • This decentralized approach enhances fault tolerance in dynamic robotic systems.
  • The findings are applicable to various fields, including environmental sensing and coordinated robotics.