Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm
Abstract With the advancement of unmanned aerial vehicle (UAV) technology, UAVs, such as multi-rotor drones, have found widespread application in wireless sensor networks. In scenarios where multiple UAVs collaborate to gather sensor data from the field, it is essential to establish a path planning...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99001-z |
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| author | Yuanhang Qi Haoran Jiang Gewen Huang Liang Yang Fujie Wang Yunjian Xu |
| author_facet | Yuanhang Qi Haoran Jiang Gewen Huang Liang Yang Fujie Wang Yunjian Xu |
| author_sort | Yuanhang Qi |
| collection | DOAJ |
| description | Abstract With the advancement of unmanned aerial vehicle (UAV) technology, UAVs, such as multi-rotor drones, have found widespread application in wireless sensor networks. In scenarios where multiple UAVs collaborate to gather sensor data from the field, it is essential to establish a path planning model that incorporates an accurate energy consumption model for these UAVs. The power consumption of a multi-rotor drone varies depending on its flight state. When UAVs traverse various locations, it is not only the power required for steady-level flight that must be considered, but also the power necessary for acceleration, deceleration, climbing, and turning. This paper presents a path planning model for multiple UAVs, termed the Multi-UAV Path Planning Considering Multiple Energy Consumptions (MUAVPP-MEC). The solution derived adheres to the constraint that UAV flight energy consumption should not exceed the maximum stored energy, with the goal of minimizing the total flight time across all UAV paths. To tackle the MUAVPP-MEC, this study proposes an improved Bee Foraging Learning Particle Swarm Optimization algorithm (IBFLPSO), which integrates the bee-foraging algorithm into the particle swarm optimization framework. The IBFLPSO facilitates an efficient real-number encoding and greedy segmenting sequence decoding strategy, translating the solution space of the problem into the search space of the algorithm. To improve the optimization capabilities of the algorithm, IBFLPSO utilizes the energy-constrained 2-opt as a local search operator. In Experiment 1, the proposed model and algorithm are validated through three distinct case studies, demonstrating the stability and efficacy of the methods. It is clearly observed that as the number of collection points increases, both the total cruising time and energy consumption of the model rise significantly, thus confirming the accuracy of the model. In Experiment 2, when compared with four other algorithms, IBFLPSO outperforms them in both the optimal and average solutions. Specifically, the optimal solution of IBFLPSO is 54.64%, 49.45%, 25.78%, and 22.92% better than those of the traditional PSO algorithm, PSO-2OPT algorithm, GA, and BFLPSO, respectively. |
| format | Article |
| id | doaj-art-8d6c626f3e054fb6bceb33fab8bc4be2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8d6c626f3e054fb6bceb33fab8bc4be22025-08-20T01:47:33ZengNature PortfolioScientific Reports2045-23222025-04-0115111610.1038/s41598-025-99001-zMulti-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithmYuanhang Qi0Haoran Jiang1Gewen Huang2Liang Yang3Fujie Wang4Yunjian Xu5School of Computer Science, University of Electronic Science and Technology of China, Zhongshan InstituteSchool of Computer Science, University of Electronic Science and Technology of China, Zhongshan InstituteInformation and Network Center, Jiaying UniversitySchool of Computer Science, University of Electronic Science and Technology of China, Zhongshan InstituteSchool of Excellent Engineering, Dongguan University of TechnologySchool of Intelligent Engineering, Guangdong AIB PolytechnicAbstract With the advancement of unmanned aerial vehicle (UAV) technology, UAVs, such as multi-rotor drones, have found widespread application in wireless sensor networks. In scenarios where multiple UAVs collaborate to gather sensor data from the field, it is essential to establish a path planning model that incorporates an accurate energy consumption model for these UAVs. The power consumption of a multi-rotor drone varies depending on its flight state. When UAVs traverse various locations, it is not only the power required for steady-level flight that must be considered, but also the power necessary for acceleration, deceleration, climbing, and turning. This paper presents a path planning model for multiple UAVs, termed the Multi-UAV Path Planning Considering Multiple Energy Consumptions (MUAVPP-MEC). The solution derived adheres to the constraint that UAV flight energy consumption should not exceed the maximum stored energy, with the goal of minimizing the total flight time across all UAV paths. To tackle the MUAVPP-MEC, this study proposes an improved Bee Foraging Learning Particle Swarm Optimization algorithm (IBFLPSO), which integrates the bee-foraging algorithm into the particle swarm optimization framework. The IBFLPSO facilitates an efficient real-number encoding and greedy segmenting sequence decoding strategy, translating the solution space of the problem into the search space of the algorithm. To improve the optimization capabilities of the algorithm, IBFLPSO utilizes the energy-constrained 2-opt as a local search operator. In Experiment 1, the proposed model and algorithm are validated through three distinct case studies, demonstrating the stability and efficacy of the methods. It is clearly observed that as the number of collection points increases, both the total cruising time and energy consumption of the model rise significantly, thus confirming the accuracy of the model. In Experiment 2, when compared with four other algorithms, IBFLPSO outperforms them in both the optimal and average solutions. Specifically, the optimal solution of IBFLPSO is 54.64%, 49.45%, 25.78%, and 22.92% better than those of the traditional PSO algorithm, PSO-2OPT algorithm, GA, and BFLPSO, respectively.https://doi.org/10.1038/s41598-025-99001-zImproved bee foraging learning particle swarm optimizationParticle swarm optimizationUAVPath planning |
| spellingShingle | Yuanhang Qi Haoran Jiang Gewen Huang Liang Yang Fujie Wang Yunjian Xu Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm Scientific Reports Improved bee foraging learning particle swarm optimization Particle swarm optimization UAV Path planning |
| title | Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| title_full | Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| title_fullStr | Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| title_full_unstemmed | Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| title_short | Multi-UAV path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| title_sort | multi uav path planning considering multiple energy consumptions via an improved bee foraging learning particle swarm optimization algorithm |
| topic | Improved bee foraging learning particle swarm optimization Particle swarm optimization UAV Path planning |
| url | https://doi.org/10.1038/s41598-025-99001-z |
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