Coverage path planning considering the cell number and starting position

The coverage tasks of mobile robots are evolving towards large-scale and intelligent directions, demanding urgent requirements for the coverage efficiency and environmental adaptability of coverage path planning. To address the inadequate adaptability of the traditional boustrophedon cellular decomp...

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Main Authors: MA Mingyan, HUANG Sirong, DENG Renhui, WU Lei, HE Li
Format: Article
Language:zho
Published: Editorial Office of Journal of XPU 2024-08-01
Series:Xi'an Gongcheng Daxue xuebao
Subjects:
Online Access:http://journal.xpu.edu.cn/en/#/digest?ArticleID=1477
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author MA Mingyan
HUANG Sirong
DENG Renhui
WU Lei
HE Li
author_facet MA Mingyan
HUANG Sirong
DENG Renhui
WU Lei
HE Li
author_sort MA Mingyan
collection DOAJ
description The coverage tasks of mobile robots are evolving towards large-scale and intelligent directions, demanding urgent requirements for the coverage efficiency and environmental adaptability of coverage path planning. To address the inadequate adaptability of the traditional boustrophedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decomposition method, a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establishing the mapping relationship between the starting and ending positions of cells, a genetic algorithm was employed to optimize the selection of cells′ starting positions and the global access order. The research results show that: 1) when processing maps with dimensions of 1 300 pixels, the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area; 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total operation time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99.91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper.
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spelling doaj-art-e56a293c8f5d4db3a2974dc9bf5523102025-08-20T03:09:52ZzhoEditorial Office of Journal of XPUXi'an Gongcheng Daxue xuebao1674-649X2024-08-013841810.13338/j.issn.1674-649x.2024.04.001Coverage path planning considering the cell number and starting positionMA Mingyan0HUANG Sirong1DENG Renhui2WU Lei3HE Li4School of Mechatronic Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Mechatronic Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Mechatronic Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaChina Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 511370, ChinaDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, ChinaThe coverage tasks of mobile robots are evolving towards large-scale and intelligent directions, demanding urgent requirements for the coverage efficiency and environmental adaptability of coverage path planning. To address the inadequate adaptability of the traditional boustrophedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decomposition method, a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establishing the mapping relationship between the starting and ending positions of cells, a genetic algorithm was employed to optimize the selection of cells′ starting positions and the global access order. The research results show that: 1) when processing maps with dimensions of 1 300 pixels, the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area; 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total operation time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99.91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper.http://journal.xpu.edu.cn/en/#/digest?ArticleID=1477coverage path planningcellular decomposition methodgenetic algorithmmonotonic polygonstarting position optimization
spellingShingle MA Mingyan
HUANG Sirong
DENG Renhui
WU Lei
HE Li
Coverage path planning considering the cell number and starting position
Xi'an Gongcheng Daxue xuebao
coverage path planning
cellular decomposition method
genetic algorithm
monotonic polygon
starting position optimization
title Coverage path planning considering the cell number and starting position
title_full Coverage path planning considering the cell number and starting position
title_fullStr Coverage path planning considering the cell number and starting position
title_full_unstemmed Coverage path planning considering the cell number and starting position
title_short Coverage path planning considering the cell number and starting position
title_sort coverage path planning considering the cell number and starting position
topic coverage path planning
cellular decomposition method
genetic algorithm
monotonic polygon
starting position optimization
url http://journal.xpu.edu.cn/en/#/digest?ArticleID=1477
work_keys_str_mv AT mamingyan coveragepathplanningconsideringthecellnumberandstartingposition
AT huangsirong coveragepathplanningconsideringthecellnumberandstartingposition
AT dengrenhui coveragepathplanningconsideringthecellnumberandstartingposition
AT wulei coveragepathplanningconsideringthecellnumberandstartingposition
AT heli coveragepathplanningconsideringthecellnumberandstartingposition