Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm
When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/2/86 |
| _version_ | 1797483873656897536 |
|---|---|
| author | Jinbiao Yuan Zhenbao Liu Yeda Lian Lulu Chen Qiang An Lina Wang Bodi Ma |
| author_facet | Jinbiao Yuan Zhenbao Liu Yeda Lian Lulu Chen Qiang An Lina Wang Bodi Ma |
| author_sort | Jinbiao Yuan |
| collection | DOAJ |
| description | When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution. |
| first_indexed | 2024-03-09T22:54:18Z |
| format | Article |
| id | doaj.art-884c430720c44403b24c176314e1130d |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T22:54:18Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-884c430720c44403b24c176314e1130d2023-11-23T18:14:27ZengMDPI AGAerospace2226-43102022-02-01928610.3390/aerospace9020086Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic AlgorithmJinbiao Yuan0Zhenbao Liu1Yeda Lian2Lulu Chen3Qiang An4Lina Wang5Bodi Ma6School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaResearch Institute of Aero-Engine, Beihang University, Beijing 100190, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaWhen performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.https://www.mdpi.com/2226-4310/9/2/86UAVarea coverageGApath planningGPSA |
| spellingShingle | Jinbiao Yuan Zhenbao Liu Yeda Lian Lulu Chen Qiang An Lina Wang Bodi Ma Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm Aerospace UAV area coverage GA path planning GPSA |
| title | Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm |
| title_full | Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm |
| title_fullStr | Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm |
| title_full_unstemmed | Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm |
| title_short | Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm |
| title_sort | global optimization of uav area coverage path planning based on good point set and genetic algorithm |
| topic | UAV area coverage GA path planning GPSA |
| url | https://www.mdpi.com/2226-4310/9/2/86 |
| work_keys_str_mv | AT jinbiaoyuan globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT zhenbaoliu globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT yedalian globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT luluchen globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT qiangan globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT linawang globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm AT bodima globaloptimizationofuavareacoveragepathplanningbasedongoodpointsetandgeneticalgorithm |