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Improved genetic algorithm based on bi-level co-evolution for coverage path planning in irregular region.

Guanzhong Chen1, Yufeng Du2, Xiaoming Xi3

  • 1School of Computer Science and Technology, Shandong Jianzhu University, Fengming Road, Jinan, 250101, Shandong, China.

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This study introduces an improved genetic algorithm for irregular region coverage path planning. The novel bi-level co-evolutionary strategy enhances efficiency and optimizes robot search paths.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Coverage path planning (CPP) is crucial for robot search tasks in complex environments.
  • Planning efficient paths in irregular regions presents significant computational challenges.
  • Existing methods often struggle with optimizing path order and coverage simultaneously.

Purpose of the Study:

  • To propose an improved genetic algorithm for coverage path planning (IGA-CPP) in irregular regions.
  • To enhance path optimization through a novel bi-level co-evolutionary strategy.
  • To improve the efficiency and convergence speed of genetic algorithms for CPP.

Main Methods:

  • Region decomposition into manageable sub-regions.
  • Bi-level co-evolutionary strategy for optimizing sub-region order and coverage paths.
  • Genetic algorithm framework with linearly decreasing population size for fast convergence.
  • Path length optimization as the primary objective function.

Main Results:

  • IGA-CPP demonstrated superior efficiency compared to other algorithms in simulation experiments.
  • Optimal control parameters for IGA-CPP were identified through extensive comparison experiments.
  • The bi-level co-evolutionary approach effectively addressed the complexity of sub-region path planning.
  • Fast convergence was achieved by reducing population size iteratively.

Conclusions:

  • IGA-CPP is a workable and efficient method for optimizing coverage paths in irregular regions.
  • The proposed bi-level co-evolutionary strategy significantly improves genetic algorithm performance for CPP.
  • This approach offers a robust solution for robot search and exploration tasks.