Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles
In some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path pl...
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MDPI AG
2022-08-01
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Series: | Drones |
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Online Access: | https://www.mdpi.com/2504-446X/6/8/203 |
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author | Wenxin Le Zhentao Xue Jian Chen Zichao Zhang |
author_facet | Wenxin Le Zhentao Xue Jian Chen Zichao Zhang |
author_sort | Wenxin Le |
collection | DOAJ |
description | In some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs). Firstly, the energy flow efficiency based on the energy model is proposed to evaluate the energy utilization efficiency during the operation. Moreover, for the areas with and without obstacles, the coverage path optimization model is proposed based on the undirected graph search method. The constraint equation is defined to restrict the UAV from accessing the undirected graph according to certain rules. A mixed integer linear programming (MILP) model is proposed to determine the flight path of each UAV with the objective of minimizing operation time. Through the simulation experiment, compared with the Boustrophedon Cellular Decomposition method for coverage path planning, it is seen that the completion time is greatly improved. In addition, considering the impact of the attitude angle of the solar powered UAV when turning, the operation time and the total energy flow efficiency are defined as the optimization objective. The bi-objective model equation is established to solve the problem of the CPP. A large number of simulation experiments show that the optimization model in this paper selects different optimization objectives and applies to different shapes of areas to be covered, which has wide applicability and strong feasibility. |
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format | Article |
id | doaj.art-cb32ab9c963f46fab3e20e8377316ffa |
institution | Directory Open Access Journal |
issn | 2504-446X |
language | English |
last_indexed | 2024-03-09T04:33:19Z |
publishDate | 2022-08-01 |
publisher | MDPI AG |
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series | Drones |
spelling | doaj.art-cb32ab9c963f46fab3e20e8377316ffa2023-12-03T13:33:21ZengMDPI AGDrones2504-446X2022-08-016820310.3390/drones6080203Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial VehiclesWenxin Le0Zhentao Xue1Jian Chen2Zichao Zhang3College of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaIn some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs). Firstly, the energy flow efficiency based on the energy model is proposed to evaluate the energy utilization efficiency during the operation. Moreover, for the areas with and without obstacles, the coverage path optimization model is proposed based on the undirected graph search method. The constraint equation is defined to restrict the UAV from accessing the undirected graph according to certain rules. A mixed integer linear programming (MILP) model is proposed to determine the flight path of each UAV with the objective of minimizing operation time. Through the simulation experiment, compared with the Boustrophedon Cellular Decomposition method for coverage path planning, it is seen that the completion time is greatly improved. In addition, considering the impact of the attitude angle of the solar powered UAV when turning, the operation time and the total energy flow efficiency are defined as the optimization objective. The bi-objective model equation is established to solve the problem of the CPP. A large number of simulation experiments show that the optimization model in this paper selects different optimization objectives and applies to different shapes of areas to be covered, which has wide applicability and strong feasibility.https://www.mdpi.com/2504-446X/6/8/203solar powered UAVenergy flow efficiencycoverage path planningmixed integer linear programmingcoverage path optimization modelbi-objective optimization |
spellingShingle | Wenxin Le Zhentao Xue Jian Chen Zichao Zhang Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles Drones solar powered UAV energy flow efficiency coverage path planning mixed integer linear programming coverage path optimization model bi-objective optimization |
title | Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles |
title_full | Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles |
title_fullStr | Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles |
title_full_unstemmed | Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles |
title_short | Coverage Path Planning Based on the Optimization Strategy of Multiple Solar Powered Unmanned Aerial Vehicles |
title_sort | coverage path planning based on the optimization strategy of multiple solar powered unmanned aerial vehicles |
topic | solar powered UAV energy flow efficiency coverage path planning mixed integer linear programming coverage path optimization model bi-objective optimization |
url | https://www.mdpi.com/2504-446X/6/8/203 |
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