Complete Coverage Path Planning for Horticultural Electric Tractors Based on an Improved Genetic Algorithm

Gaogao Shang; Gang Liu; Peng Zhu; Jiangyi Han

doi:10.6180/jase.202106_24(3).0020

Complete Coverage Path Planning for Horticultural Electric Tractors Based on an Improved Genetic Algorithm

Gaogao Shang¹ , Gang Liu¹ , Peng Zhu¹ , and Jiangyi Han This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹College of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, P.R. China

Received: November 14, 2020
Accepted: January 14, 2021
Publication Date: June 1, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202106_24(3).0020

ABSTRACT

Complete coverage path planning is one of the key technologies to realize the autonomous action of intelligent agricultural machinery in complex environments. In this paper, a spatial model of a 2.5D working environment is established by a grid method, and an algorithm of complete coverage path planning for electric tractors is also studied based on an improved genetic algorithm. To reduce the driving energy consumption of tractors, a multi-objective fitness function of the flat driving path length, the number of turns and the total elevation difference of the genetic algorithm is established by analyzing the influencing factors of energy consumption. Crossover and mutation operators are also improved to enhance the execution efficiency of the algorithm and reduce the driving repetition rate. Simulation and comparison experiments are carried out in parallel as well. The test results demonstrate that, in contrast with the traditional genetic algorithm and other intelligent optimization methods, the complete coverage path planning based upon the improved genetic algorithm proposed in this paper can prominently reduce the energy consumption and driving repetition rate of the electric tractor and improve the convergence speed and optimization ability.

Keywords: Grid Method; Optimation; Multi-objective; Genetic Algorithm

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