A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning
Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/644 |
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| author | Bei Liu Yuefeng Cai Duantengchuan Li Ke Lin Guanghui Xu |
| author_facet | Bei Liu Yuefeng Cai Duantengchuan Li Ke Lin Guanghui Xu |
| author_sort | Bei Liu |
| collection | DOAJ |
| description | Path planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability. |
| format | Article |
| id | doaj-art-257911d78e8d4182ae3b7e3bcd383486 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-257911d78e8d4182ae3b7e3bcd3834862024-11-26T18:00:41ZengMDPI AGDrones2504-446X2024-11-0181164410.3390/drones8110644A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path PlanningBei Liu0Yuefeng Cai1Duantengchuan Li2Ke Lin3Guanghui Xu4School of Electrical and Electronics Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Information Management, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Electrical and Electronics Engineering, Hubei University of Technology, Wuhan 430068, ChinaPath planning is a fundamental research issue for enabling autonomous flight in unmanned aerial vehicles (UAVs). An effective path planning algorithm can greatly improve the operational efficiency of UAVs in complex environments like urban and mountainous areas, thus offering more extensive coverage for various tasks. However, existing path planning algorithms often encounter problems such as high computational costs and a tendency to become trapped in local optima in complex 3D environments with multiple constraints. To tackle these problems, this paper introduces a hybrid multi-strategy artificial rabbits optimization (HARO) for efficient and stable UAV path planning in complex environments. To realistically simulate complex scenarios, we introduce spherical and cylindrical obstacle models. The HARO algorithm balances exploration and exploitation phases using a dual exploration switching strategy and a population migration memory mechanism, enhancing search performance and avoiding local optima. Additionally, a key point retention trajectory optimization strategy is proposed to reduce redundant path points, thus lowering flight costs. Experimental results confirm the HARO algorithm’s superior search performance, planning more efficient and stable paths in complex environments. The key point retention strategy effectively reduces flight costs during trajectory optimization, thereby enhancing adaptability.https://www.mdpi.com/2504-446X/8/11/644UAV path planningunmanned aerial vehicleartificial rabbits optimizationhybrid algorithmtrajectory optimization |
| spellingShingle | Bei Liu Yuefeng Cai Duantengchuan Li Ke Lin Guanghui Xu A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning Drones UAV path planning unmanned aerial vehicle artificial rabbits optimization hybrid algorithm trajectory optimization |
| title | A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning |
| title_full | A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning |
| title_fullStr | A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning |
| title_full_unstemmed | A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning |
| title_short | A Hybrid ARO Algorithm and Key Point Retention Strategy Trajectory Optimization for UAV Path Planning |
| title_sort | hybrid aro algorithm and key point retention strategy trajectory optimization for uav path planning |
| topic | UAV path planning unmanned aerial vehicle artificial rabbits optimization hybrid algorithm trajectory optimization |
| url | https://www.mdpi.com/2504-446X/8/11/644 |
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