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Complete-Coverage Path-Planning Algorithm Based on Transition Probability and Learning Perturbation Operator.

Xia Wang1,2, Gongshuo Han1, Jianing Tang1,2

  • 1School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

A new complete coverage path planning algorithm (CCPP-TPLP) optimizes robotic paths by reducing length and repetition. This method enhances planning efficiency and quality in various environments.

Keywords:
complete-coverage path-planninginitialization strategyperturbation and learningpopulation hierarchytransition probability

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Complete coverage path planning (CCPP) is crucial for robotic tasks like cleaning and mapping.
  • Existing algorithms often struggle with optimizing path length and minimizing repeated movements.

Purpose of the Study:

  • To propose a novel CCPP algorithm, CCPP-TPLP, that enhances path efficiency.
  • To reduce path length and repetition rate in robotic path planning.

Main Methods:

  • Developed a CCPP algorithm integrating transition probability and learning perturbation.
  • Constructed distance and transition probability matrices based on grid adjacency.
  • Utilized a greedy strategy for optimal initial path generation.
  • Implemented subgroup-based learning perturbation operations for path optimization.

Main Results:

  • CCPP-TPLP demonstrated superior performance compared to five other algorithms.
  • The algorithm effectively optimized path node selection.
  • Significant reductions in total path length and repetition rate were observed.
  • Improved planning efficiency and quality were achieved in diverse environments, including those with height information.

Conclusions:

  • CCPP-TPLP offers an effective solution for efficient robotic complete coverage path planning.
  • The algorithm's ability to optimize paths makes it suitable for practical applications like agricultural robotics.