Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot
Author:
Wu Xizhi1ORCID, Bai Jinqiang1, Hao Fengqi1, Cheng Guanghe1, Tang Yongwei2, Li Xiuhua1
Affiliation:
1. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), QiLu University of Technology (Shandong Academy of Sciences), 19 Keyuan Road, Jinan 250014, China 2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China
Abstract
The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.
Funder
Young Scientists Fund of the National Natural Science Foundation of China Major Science and Technology Innovation Project of Shan-dong Province Shandong Province Natural Science Youth Foundation of China Qilu University of Technology (Shandong Academy of Sciences) Science-Education-Industry integration pilot project plan
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
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