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

Abstract

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.