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Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning.

Yijie Qin1, Lei Fu1, Dingxin He1

  • 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430070, China.

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Summary
This summary is machine-generated.

This study introduces efficient multi-agent path planning algorithms for complete region coverage. The approach divides complex areas into sub-regions, using rapidly exploring random trees (RRT) for robust path planning.

Keywords:
complete coveragemCPPminimum coverage pathsmulti-agent system

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Multi-agent path planning (mCPP) is computationally complex.
  • Efficient coverage of complex target regions requires advanced algorithms.

Purpose of the Study:

  • To develop efficient algorithms for multi-agent traversal exploration and path coverage.
  • To reduce the combinatorial complexity of the multi-agent path planning problem.

Main Methods:

  • Transforming the mCPP into single-agent sub-problems by region partitioning.
  • Utilizing the rapidly exploring random trees (RRT) algorithm for closed-loop path planning.
  • Applying geodesic distance and Dijkstra algorithm for improved partitioning and path robustness.

Main Results:

  • Reduced combinatorial complexity through balanced sub-region division.
  • Ensured continuous exploration and repeated node visits using RRT.
  • Improved partitioning efficiency and reduced path turns with geodesic distance and Dijkstra.

Conclusions:

  • The proposed algorithm achieves complete target area coverage with high applicability to various region shapes.
  • It ensures reasonable task distribution, minimal average waiting time, and sustainable exploration.
  • The method offers a robust and efficient solution for multi-agent path planning challenges.