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Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance

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  • 1School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a new multirobot coverage path planning (MCPP) framework for surveillance, using advanced algorithms like genetic algorithms (GAs) and ant colony optimization (ACO). Area-based MCPP excels in path planning efficiency, while path-based MCPP offers better coverage balance.

Keywords:
ant colony optimizationgenetic algorithmmultirobot coverage path planningtraveling salesman problem

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Multirobot Coverage Path Planning (MCPP) is crucial for surveillance but often limited by naive approaches, especially when considering robot sensing range (SR).
  • Existing MCPP methods primarily focus on cleaning tasks, lacking comprehensive strategies for surveillance applications that incorporate SR.

Purpose of the Study:

  • To systematically explore hierarchical designs for MCPP tailored to surveillance applications, incorporating the sensing range (SR) factor.
  • To propose a general MCPP framework that comprehensively addresses both path-based and area-based structures, utilizing advanced algorithms for enhanced planning.

Main Methods:

  • Developed six distinct MCPP methods: two area-based (hierarchical max-flow routing with GA or ACO for Traveling Salesman Problem (TSP) within areas) and four path-based (global-local structures using GA and ACO combinations).
  • Incorporated advanced TSP solvers like Genetic Algorithms (GAs) and Ant Colony Optimization (ACO) instead of naive approaches.
  • Introduced ACO- or GA-based local planning in path-based methods to improve path quality and prevent degradation in global planning outcomes.

Main Results:

  • Area-based MCPP approaches demonstrated superior performance in execution time and total path length compared to path-based methods.
  • Path-based MCPP methods exhibited an advantage with smaller idle times.
  • The proposed area-based MCPP method excels in path planning efficiency, while the path-based MCPP method shows better coverage balance.

Conclusions:

  • The choice between area-based and path-based MCPP structures depends on specific application requirements, balancing efficiency, path length, and coverage.
  • Leveraging the strengths of both methodologies allows for efficient MCPP execution in surveillance tasks.
  • Future work will focus on adapting these MCPP structures for diverse real-world conditions to identify the most suitable approach for specific applications.