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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments.

Lin Li1, Dianxi Shi2,3, Songchang Jin2,3

  • 1College of Computer, National University of Defense Technology, Changsha 410003, China.

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|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces two algorithms for multi-robot coverage path planning: an exact method (EDM) for optimal paths and a heuristic method (CDM) for efficiency. EDM excels in small areas, while CDM balances speed and computation in larger environments.

Keywords:
Dubins robotscoverage path planningpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Operations Research

Background:

  • Multi-robot coverage path planning (MCPP) is crucial for applications like aerial monitoring and search and rescue.
  • Existing algorithms struggle with balancing path optimality, area division, and computational complexity.

Purpose of the Study:

  • To develop and evaluate novel algorithms for Dubins multi-robot coverage path planning in known environments.
  • To address limitations of existing exact and heuristic MCPP methods.

Main Methods:

  • Proposed an exact Dubins multi-robot coverage path planning (EDM) algorithm using mixed linear integer programming (MILP) to find the shortest coverage path.
  • Developed a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm employing a credit model and tree partitioning for efficiency.

Main Results:

  • EDM achieved the shortest coverage time in small-scale environments.
  • CDM demonstrated shorter coverage and computation times in large-scale environments.
  • Both EDM and CDM were validated on a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model.

Conclusions:

  • EDM offers optimal solutions for Dubins MCPP in specific scenarios.
  • CDM provides an effective trade-off between coverage time and computational complexity for larger MCPP problems.
  • The proposed algorithms are feasible for real-world UAV applications.