Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities
Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact cos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/6/367 |
| _version_ | 1827737769011576832 |
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| author | Hongyu Xiang Yuhang Han Nan Pan Miaohan Zhang Zhenwei Wang |
| author_facet | Hongyu Xiang Yuhang Han Nan Pan Miaohan Zhang Zhenwei Wang |
| author_sort | Hongyu Xiang |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems. |
| first_indexed | 2024-03-11T02:34:06Z |
| format | Article |
| id | doaj.art-d7a139c0011145b7b497a3824a095662 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T02:34:06Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-d7a139c0011145b7b497a3824a0956622023-11-18T10:04:06ZengMDPI AGDrones2504-446X2023-06-017636710.3390/drones7060367Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large CitiesHongyu Xiang0Yuhang Han1Nan Pan2Miaohan Zhang3Zhenwei Wang4Faculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautical, Kunming University of Science and Technology, Kunming 650500, ChinaUnmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.https://www.mdpi.com/2504-446X/7/6/367lightning search algorithmmulti-layer nesting strategypath planningquadcopter UAVurban patrol |
| spellingShingle | Hongyu Xiang Yuhang Han Nan Pan Miaohan Zhang Zhenwei Wang Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities Drones lightning search algorithm multi-layer nesting strategy path planning quadcopter UAV urban patrol |
| title | Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities |
| title_full | Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities |
| title_fullStr | Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities |
| title_full_unstemmed | Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities |
| title_short | Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities |
| title_sort | study on multi uav cooperative path planning for complex patrol tasks in large cities |
| topic | lightning search algorithm multi-layer nesting strategy path planning quadcopter UAV urban patrol |
| url | https://www.mdpi.com/2504-446X/7/6/367 |
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