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

## TL;DR

This paper introduces a new algorithm for robotic path planning that reduces path length and repetition by using transition probabilities and learning perturbation.

## Contribution

The novel CCPP-TPLP algorithm combines transition probability and learning perturbation to improve complete coverage path planning efficiency and quality.

## Key findings

- CCPP-TPLP reduces total path length and repetition rate in robotic coverage tasks.
- The algorithm improves planning efficiency and quality compared to five other algorithms.
- It performs well in various map environments, including those with height information.

## Abstract

To achieve shorter path length and lower repetition rate for robotic complete coverage path planning, a complete-coverage path-planning algorithm based on transition probability and learning perturbation operator (CCPP-TPLP) is proposed. Firstly, according to the adjacency information between nodes, the distance matrix and transition probability matrix of the accessible grid are established, and the optimal initialization path is generated by applying greedy strategy on the transition probability matrix. Secondly, the population is divided into four subgroups, and different degrees of learning perturbation operations are carried out on subgroups to update each path in the population. CCPP-TPLP was tested against five algorithms in different map environments and in the working map environment of electric tractors with height information The results show that CCPP-TPLP can optimize the selection of path nodes, reduce the total length and repetition rate of the path, and significantly improve the planning efficiency and quality of complete coverage path planning.