Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm
In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/7/468 |
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| author | Zhekun Cheng Jueying Yang Jinfeng Sun Liangyu Zhao |
| author_facet | Zhekun Cheng Jueying Yang Jinfeng Sun Liangyu Zhao |
| author_sort | Zhekun Cheng |
| collection | DOAJ |
| description | In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability and ensuring safety. To overcome these challenges, this review presents a comprehensive summary of various trajectory planning techniques currently applied to UAVs based on the emergence of intelligent algorithms, which enhance the adaptability and learning ability of UAVs and offer innovative solutions for their application in complex environments. Firstly, the characteristics of different UAV types, including fixed-wing, multi-rotor UAV, single-rotor UAV, and tilt-rotor UAV, are introduced. Secondly, the key constraints of trajectory planning in complex environments are summarized. Thirdly, the research trend from 2010 to 2024, together with the implementation, advantages, and existing problems of machine learning, evolutionary algorithms, and swarm intelligence, are compared. Based on these algorithms, the related applications of UAVs in complex environments, including transportation, inspection, and other tasks, are summarized. Ultimately, this review provides practical guidance for developing intelligent trajectory planning methods for UAVs to achieve the minimal amount of time spent on computation, efficient dynamic collision avoidance, and superior task completion ability. |
| format | Article |
| id | doaj-art-a33ae77c989f4a35a184b3f50e2fe1ae |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-a33ae77c989f4a35a184b3f50e2fe1ae2025-08-20T02:45:55ZengMDPI AGDrones2504-446X2025-07-019746810.3390/drones9070468Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent AlgorithmZhekun Cheng0Jueying Yang1Jinfeng Sun2Liangyu Zhao3School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Medical Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaIn recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability and ensuring safety. To overcome these challenges, this review presents a comprehensive summary of various trajectory planning techniques currently applied to UAVs based on the emergence of intelligent algorithms, which enhance the adaptability and learning ability of UAVs and offer innovative solutions for their application in complex environments. Firstly, the characteristics of different UAV types, including fixed-wing, multi-rotor UAV, single-rotor UAV, and tilt-rotor UAV, are introduced. Secondly, the key constraints of trajectory planning in complex environments are summarized. Thirdly, the research trend from 2010 to 2024, together with the implementation, advantages, and existing problems of machine learning, evolutionary algorithms, and swarm intelligence, are compared. Based on these algorithms, the related applications of UAVs in complex environments, including transportation, inspection, and other tasks, are summarized. Ultimately, this review provides practical guidance for developing intelligent trajectory planning methods for UAVs to achieve the minimal amount of time spent on computation, efficient dynamic collision avoidance, and superior task completion ability.https://www.mdpi.com/2504-446X/9/7/468unmanned aerial vehicletrajectory planningintelligent algorithmcomplex environments |
| spellingShingle | Zhekun Cheng Jueying Yang Jinfeng Sun Liangyu Zhao Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm Drones unmanned aerial vehicle trajectory planning intelligent algorithm complex environments |
| title | Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm |
| title_full | Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm |
| title_fullStr | Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm |
| title_full_unstemmed | Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm |
| title_short | Trajectory Planning of Unmanned Aerial Vehicles in Complex Environments Based on Intelligent Algorithm |
| title_sort | trajectory planning of unmanned aerial vehicles in complex environments based on intelligent algorithm |
| topic | unmanned aerial vehicle trajectory planning intelligent algorithm complex environments |
| url | https://www.mdpi.com/2504-446X/9/7/468 |
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