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Journal articles on the topic 'The UAV path planning problem'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Wei, Zhiqiang, Yu Hu, Zhiyan Dong, et al. "UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter." Journal of Physics: Conference Series 2755, no. 1 (2024): 012031. http://dx.doi.org/10.1088/1742-6596/2755/1/012031.

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Abstract Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking t

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2

Wang, Xing, Jeng-Shyang Pan, Qingyong Yang, Lingping Kong, Václav Snášel, and Shu-Chuan Chu. "Modified Mayfly Algorithm for UAV Path Planning." Drones 6, no. 5 (2022): 134. http://dx.doi.org/10.3390/drones6050134.

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The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV con

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3

Wu, Yan, Mingtao Nie, Xiaolei Ma, Yicong Guo, and Xiaoxiong Liu. "Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning." Drones 7, no. 10 (2023): 606. http://dx.doi.org/10.3390/drones7100606.

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Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed

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Liu, Yongbei, Naiming Qi, Weiran Yao, Jun Zhao, and Song Xu. "Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach." Applied Sciences 10, no. 12 (2020): 4154. http://dx.doi.org/10.3390/app10124154.

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To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the pr

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Chen, Xiaotong, Qin Li, Ronghao Li, Xiangyuan Cai, Jiangnan Wei, and Hongying Zhao. "UAV Network Path Planning and Optimization Using a Vehicle Routing Model." Remote Sensing 15, no. 9 (2023): 2227. http://dx.doi.org/10.3390/rs15092227.

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Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV netw

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Fu, Zhangjie, Jingnan Yu, Guowu Xie, Yiming Chen, and Yuanhang Mao. "A Heuristic Evolutionary Algorithm of UAV Path Planning." Wireless Communications and Mobile Computing 2018 (September 9, 2018): 1–11. http://dx.doi.org/10.1155/2018/2851964.

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With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery l

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7

Guo, Yifan, and Zhiping Liu. "UAV Path Planning Based on Deep Reinforcement Learning." International Journal of Advanced Network, Monitoring and Controls 8, no. 3 (2023): 81–88. http://dx.doi.org/10.2478/ijanmc-2023-0068.

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Abstract Path planning is one of the very important aspects of UAV navigation control, which refers to the UAV searching for an optimal or near-optimal route from the starting point to the end point according to the performance indexes such as time, distance, et al. The path planning problem has a long history and has more abundant algorithms. The path planning problem has a long history and a rich set of algorithms, but most of the current algorithms require a known environment, however, in most cases, the environment model is difficult to describe and obtain, and the algorithms perform less

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8

Xiao, Chang, Huiliao Yang, and Bo Zhang. "Multi-Unmanned Aerial Vehicle Path Planning Based on Improved Nutcracker Optimization Algorithm." Drones 9, no. 2 (2025): 116. https://doi.org/10.3390/drones9020116.

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For the multi-UAV path planning problem, environmental modeling and an improved swarm intelligence-based optimization algorithm are discussed in this paper. Firstly, to align with reality, specific constraints of UAVs in motions, attitudes and altitudes, real-world threats such as radars and no-fly zones, and inter-UAV collisions are considered. Thus, multi-UAV path planning is transformed into a multi-objective constrained optimization problem. Accordingly, an improved nutcracker optimization algorithm is proposed to solve this problem. Through initializing with logistic chaotic mapping and t

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9

Xu, Yiqing, Jiaming Li, and Fuquan Zhang. "A UAV-Based Forest Fire Patrol Path Planning Strategy." Forests 13, no. 11 (2022): 1952. http://dx.doi.org/10.3390/f13111952.

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The application of UAVs in forest fire monitoring has attracted increasing attention. When a UAV carries out forest fire monitoring cruises in a large area of the forest, one of the main problems is planning an appropriate cruise path so that the UAV can start from the starting point, cruise the entire area with little detour, and return to the initial position within its maximum cruise distance. In this paper, we propose a flight path planning method for UAV forest fire monitoring based on a forest fire risk map. According to the forest fire risk level, the method uses the ring self-organizin

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10

Liu, Zhengqing, Xinhua Wang, and Kangyi Li. "Research on path planning of multi-rotor UAV based on improved artificial potential field method." MATEC Web of Conferences 336 (2021): 07006. http://dx.doi.org/10.1051/matecconf/202133607006.

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UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacle

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11

Lv, Fengjun, Yongbo Jian, Kai Yuan, and Yubin Lu. "Unmanned Aerial Vehicle Path Planning Method Based on Improved Dung Beetle Optimization Algorithm." Symmetry 17, no. 3 (2025): 367. https://doi.org/10.3390/sym17030367.

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To address the problem of UAV path planning in complex mountainous terrains, this paper comprehensively considers constraints such as natural mountain and obstacle collision threats, the shortest path, and flight altitude. We propose a more practical UAV path planning model that better reflects the actual UAV path planning situation in complex mountainous areas. In order to solve this model, this paper improves the traditional dung beetle optimization (DBO) algorithm and proposes an improved dung beetle optimization (IDBO) algorithm. The IDBO algorithm optimizes the population initialization m

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12

Rahman, Mamunur, Nurul I. Sarkar, and Raymond Lutui. "A Survey on Multi-UAV Path Planning: Classification, Algorithms, Open Research Problems, and Future Directions." Drones 9, no. 4 (2025): 263. https://doi.org/10.3390/drones9040263.

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Multi-UAV path planning algorithms are crucial for the successful design and operation of unmanned aerial vehicle (UAV) networks. While many network researchers have proposed UAV path planning algorithms to improve system performance, an in-depth review of multi-UAV path planning has not been fully explored yet. The purpose of this study is to survey, classify, and compare the existing multi-UAV path planning algorithms proposed in the literature over the last eight years in various scenarios. After detailing classification, we compare various multi-UAV path planning algorithms based on time c

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13

Zhang, Danyang, Xiongwei Li, Guoquan Ren, Jiangyi Yao, Kaiyan Chen, and Xi Li. "Three-Dimensional Path Planning of UAVs in a Complex Dynamic Environment Based on Environment Exploration Twin Delayed Deep Deterministic Policy Gradient." Symmetry 15, no. 7 (2023): 1371. http://dx.doi.org/10.3390/sym15071371.

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Unmanned Aerial Vehicle (UAV) path planning research refers to the UAV automatically planning an optimal path to the destination under the corresponding environment, while avoiding collision with obstacles in this process. In order to solve the problem of 3D path planning of UAV in a dynamic environment, a heuristic dynamic reward function is designed to guide the UAV. We propose the Environment Exploration Twin Delayed Deep Deterministic Policy Gradient (EE-TD3) algorithm, which combines the symmetrical 3D environment exploration coding mechanism on the basis of TD3 algorithm. The EE-TD3 algo

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14

Zeng, Sha, and Kang Liu. "Research Status and Development Trend of UAV Path Planning Algorithms." Journal of Physics: Conference Series 2283, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2283/1/012004.

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Abstract This paper introduces the basic concepts of UAV track planning, the relationship between track planning and algorithms, and focuses on the analysis and induction of several commonly used algorithms at home and abroad in recent years. The advantages and disadvantages of current path planning algorithms in different application scenarios are summarized. Finally, the future development trend of UAV path planning algorithm is prospected, and it is pointed out that real-time online path planning algorithm research, multi-UAV swarm planning algorithm, perfect path planning problem model and

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15

Wang, Wentao, Chen Ye, and Jun Tian. "SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning." Drones 7, no. 7 (2023): 452. http://dx.doi.org/10.3390/drones7070452.

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The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost functio

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16

Jiang, Congxiao, Lingang Yang, Yuqing Gao, Jie Zhao, Wenne Hou, and Fangmin Xu. "An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection." Drones 9, no. 1 (2025): 47. https://doi.org/10.3390/drones9010047.

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In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this prob

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17

Cao, Han. "Path Planning Approaches for Unmanned Aerial Vehicle." Highlights in Science, Engineering and Technology 76 (December 31, 2023): 146–52. http://dx.doi.org/10.54097/dc9y0s70.

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The difficulty of finding the ideal path from the starting point to the destination site for a UAV is one of the most essential challenges related with the deployment of unmanned aerial vehicle (UAV). Path planning algorithms are classified into traditional and intelligent algorithms in this article based on the order of discovery of the path planning methods. Intelligent algorithms are algorithms that are inspired by nature and can efficiently tackle the complex path planning problem. In this article, by introducing the different advantages of traditional algorithms and intelligent algorithms

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18

Huang, Yanxi, Jiankang Xu, Mengting Shi, and Liang Liu. "Time-Efficient Coverage Path Planning for Energy-Constrained UAV." Wireless Communications and Mobile Computing 2022 (May 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/5905809.

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Unmanned aerial vehicles (UAVs) have the characteristics of high mobility and wide coverage, making them widely used in coverage, search, and other fields. In these applications, UAV often has limited energy. Therefore, planning a time-efficient coverage path for energy-constrained UAV to cover the area of interest is the core issue. The existing coverage path planning algorithms assume that the UAV moves at a constant speed, without taking into account the cost of turns (including deceleration, turning, and acceleration), which is unrealistic. To solve the above problem, we propose a time-eff

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19

Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (2021): 3948. http://dx.doi.org/10.3390/app11093948.

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Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an aut

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20

Song, Hui, Minghan Jia, Yihang Lian, Yijing Fan, and Keshan Liang. "UAV Path Planning Based on an Improved Ant Colony Algorithm." Journal of Electronic Research and Application 6, no. 2 (2022): 10–25. http://dx.doi.org/10.26689/jera.v6i2.3808.

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Reviews and experimental verification have found that existing solution methods can be used to solve UAV path planning problems, but each approximate solution has its own advantages and disadvantages. For example, ant colony algorithm easily falls into the local optimum in the process of realizing path planning. In order to prevent too low pheromones on the longer path and too high pheromones in the shorter path, the upper and lower limits of pheromones as well as their volatile factors are set to avoid falling into the local optimum. Secondly, multi-heuristic factors are introduced, and the o

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21

Wang, Dongxing, Meijing Zhang, and Zhiyang Huang. "A Path-Planning Method Recommended for Multi-UAV Police Patrols Based on the Wolf Pack Optimization Algorithm Using CDRS and DRSS." Symmetry 17, no. 2 (2025): 208. https://doi.org/10.3390/sym17020208.

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Multi-UAV path planning for police patrols plays an important role in public security work, and while many path-planning algorithms have been applied in this area, all of them possess various degrees of shortcomings. To further improve the accuracy and efficiency of multi-UAV path planning for police patrols, this paper proposes a multi-UAV police patrol path-planning method based on an improved wolf pack optimization algorithm using the strategies of Composite Directional Raid and Dynamic Random Search (PMU-3PM-IWPA). Firstly, a multi-UAV police patrol path-planning model was constructed to r

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22

Ahmadi, S. M., H. Kebriaei, and H. Moradi. "Constrained coverage path planning: evolutionary and classical approaches." Robotica 36, no. 6 (2018): 904–24. http://dx.doi.org/10.1017/s0263574718000139.

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SUMMARYThe constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the othe

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23

Bagherian, M., and A. Alos. "3D UAV trajectory planning using evolutionary algorithms: A comparison study." Aeronautical Journal 119, no. 1220 (2015): 1271–85. http://dx.doi.org/10.1017/s0001924000011246.

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Abstract This paper focuses on the three dimensional flight path planning for an unmanned aerial vehicle (UAV) on a low altitude terrain following\terrain avoidance mission. The UAV trajectory planning problem is to compute an optimal or near-optimal trajectory for a UAV to do its mission objectives in a surviving penetration through the hostile enemy environment, considering the shape of the earth and the kinematics constraints of the UAV. Using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regard

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24

Wu, Cailong, Zhengyu Guo, Jian Zhang, Kai Mao, and Delin Luo. "Cooperative Path Planning for Multiple UAVs Based on APF B-RRT* Algorithm." Drones 9, no. 3 (2025): 177. https://doi.org/10.3390/drones9030177.

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Aiming at the path planning problem of an unmanned aerial vehicle (UAV) in a complex unknown environment, this paper proposes a cooperative path planning algorithm for multiple UAVs. Using the local environment information, several rolling path plannings are carried out by the Artificial Potential Field Bidirectional-Rapidly exploring Random Trees (APF B-RRT*) algorithm. The APF B-RRT* algorithm optimizes the search space by pre-sampling and adapts with an adaptive step while fusing with the APF algorithm for guiding sampling. Then, the generated path is trimmed and smoothed to obtain the opti

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25

Aljalaud, Faten, Heba Kurdi, and Kamal Youcef-Toumi. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review." Mathematics 11, no. 10 (2023): 2356. http://dx.doi.org/10.3390/math11102356.

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Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algori

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26

Li, Li, Hong Zhan, and Yongjing Hao. "The Online Path Planning Method of UAV Autonomous Inspection in Distribution Network." E3S Web of Conferences 256 (2021): 01047. http://dx.doi.org/10.1051/e3sconf/202125601047.

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In this paper, the problem of online path planning for autonomous inspection of distribution network lines by UAV is studied. Because the distribution lines are mostly distributed around cities, counties and mountainous areas, the lines and their surrounding environment are uncertain and dynamic. These factors will affect the safety of UAV inspection, making the off-line pre-planned path for UAV unavailable. This paper designs an improved iteration random tree algorithm (IRRT) algorithm, which can quickly plan the path of UAV in dynamic environment.

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27

Yu, Yang, and Sanghwan Lee. "Efficient Multi-UAV Path Planning for Collaborative Area Search Operations." Applied Sciences 13, no. 15 (2023): 8728. http://dx.doi.org/10.3390/app13158728.

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Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy

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28

Hu, Shengrong, Qiang Wang, Yixian Li, and Fei Wang. "A framework for multi-UAV task allocation and path planning in complex urban environments." Journal of Physics: Conference Series 2891, no. 11 (2024): 112006. https://doi.org/10.1088/1742-6596/2891/11/112006.

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Abstract The complexity of urban combat environments, the coupling of task allocation and path planning, and the existence of dynamic targets significantly increased the complexity of coordinated UAV swarm attack tasks. In this work, we proposed a multi-UAV task allocation and path planning framework inspired by the collaborative hunting behaviour of wolf packs. This framework was based on a multi-target k-winner-take-all (k-WTA) algorithm and an improved grey wolf optimization (GWO) algorithm. Firstly, for the multi-task allocation problem in unknown environments, a multi-objective k-WTA algo

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29

Wang, Jingjing, Y. F. Zhang, L. Geng, J. Y. H. Fuh, and S. H. Teo. "A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs." Unmanned Systems 03, no. 03 (2015): 205–19. http://dx.doi.org/10.1142/s2301385015500132.

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This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem

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Xiang, Fengtao, Keqin Chen, Jiongming Su, Hongfu Liu, and Wanpeng Zhang. "Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm." Wireless Communications and Mobile Computing 2021 (December 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/4312592.

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Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV pen

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Shen, Yong, Yunlou Zhu, Hongwei Kang, Xingping Sun, Qingyi Chen, and Da Wang. "UAV Path Planning Based on Multi-Stage Constraint Optimization." Drones 5, no. 4 (2021): 144. http://dx.doi.org/10.3390/drones5040144.

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Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary al

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Yu, Jiayang, Jiansheng Guo, Xiaofeng Zhang, Chuhan Zhou, and Tao Xie. "UAV Path Planning in Dynamical Environment: A Novel ICACO-IDWA Algorithm." Mathematical Problems in Engineering 2022 (December 17, 2022): 1–16. http://dx.doi.org/10.1155/2022/6802360.

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In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dyna

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Zhang, Zhe, Jian Wu, Jiyang Dai, and Cheng He. "Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats." International Journal of Aerospace Engineering 2020 (August 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/8896357.

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This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-b

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34

Qiu, Shaoming, Jikun Dai, and Dongsheng Zhao. "Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm." Biomimetics 9, no. 10 (2024): 647. http://dx.doi.org/10.3390/biomimetics9100647.

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The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning str

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Yu, Wangwang, Jun Liu, and Jie Zhou. "A Novel Sparrow Particle Swarm Algorithm (SPSA) for Unmanned Aerial Vehicle Path Planning." Scientific Programming 2021 (December 9, 2021): 1–15. http://dx.doi.org/10.1155/2021/5158304.

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Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased

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Yang, Renjie, Pan Huang, Hui Gao, et al. "A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning." Biomimetics 9, no. 4 (2024): 212. http://dx.doi.org/10.3390/biomimetics9040212.

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With the rise and development of autonomy and intelligence technologies, UAVs will have increasingly significant applications in the future. It is very important to solve the problem of low-altitude penetration of UAVs to protect national territorial security. Based on an S-57 electronic chart file, the land, island, and threat information for an actual combat environment is parsed, extracted, and rasterized to construct a marine combat environment for UAV flight simulation. To address the problem of path planning for low-altitude penetration in complex environments, a photosensitivity-enhance

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YK, Guruprasad, and NageswaraGuptha M N. "Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach." Inteligencia Artificial 25, no. 70 (2022): 77–94. http://dx.doi.org/10.4114/intartif.vol25iss70pp77-94.

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Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex i

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Chen, Shaonan, Yuhong Mo, Xiaorui Wu, Jing Xiao, and Quan Liu. "Reinforcement Learning-Based Energy-Saving Path Planning for UAVs in Turbulent Wind." Electronics 13, no. 16 (2024): 3190. http://dx.doi.org/10.3390/electronics13163190.

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The unmanned aerial vehicle (UAV) is prevalent in power inspection. However, due to a limited battery life, turbulent wind, and its motion, it brings some challenges. To address these problems, a reinforcement learning-based energy-saving path-planning algorithm (ESPP-RL) in a turbulent wind environment is proposed. The algorithm dynamically adjusts flight strategies for UAVs based on reinforcement learning to find the most energy-saving flight paths. Thus, the UAV can navigate and overcome real-world constraints in order to save energy. Firstly, an observation processing module is designed to

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Wu, Runjia, Fangqing Gu, Hai-lin Liu, and Hongjian Shi. "UAV Path Planning Based on Multicritic-Delayed Deep Deterministic Policy Gradient." Wireless Communications and Mobile Computing 2022 (March 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/9017079.

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Deep deterministic policy gradient (DDPG) algorithm is a reinforcement learning method, which has been widely used in UAV path planning. However, the critic network of DDPG is frequently updated in the training process. It leads to an inevitable overestimation problem and increases the training computational complexity. Therefore, this paper presents a multicritic-delayed DDPG method for solving the UAV path planning. It uses multicritic networks and delayed learning methods to reduce the overestimation problem of DDPG and adds noise to improve the robustness in the real environment. Moreover,

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Wang, Lisong, Xiaoliang Zhang, Pingyu Deng, Jiexiang Kang, Zhongjie Gao, and Liang Liu. "An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA." International Journal of Aerospace Engineering 2020 (August 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/3516149.

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When performing a search and rescue mission, unmanned aerial vehicles (UAVs) should continuously search targets above the mission area. In order to transfer the search and rescue information quickly and efficiently, two types of UAVs, the ferrying UAVs and the searching UAVs, are used to complete the mission. Obviously, this application scenario requires an efficient path planning method for ferrying UAVs. The existing path planning methods for ferrying UAVs usually focus on shortening the path length and ignore the different initial energy of ferrying UAVs. However, the following problem does

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Sun, Tianye, Wei Sun, Changhao Sun, and Ruofei He. "Path Planning of UAV Formations Based on Semantic Maps." Remote Sensing 16, no. 16 (2024): 3096. http://dx.doi.org/10.3390/rs16163096.

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This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from semantic maps to provide initial information on task points for UAV formations, thereby planning formation paths that meet practical requirements. Firstly, a semantic segmentation network model based on multi-scale feature extraction and fusion is employed to obtain UAV aerial semantic maps containing environmental information. Secondly, based on the semantic maps, a three-point optimization model for the optimal UAV trajectory is establis

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Liao, Chuanqi, and Zuoshi Liu. "Cooperative Path Planning of Ground-air Robots for Distributed Photovoltaic Inspection." Journal of Physics: Conference Series 2658, no. 1 (2023): 012015. http://dx.doi.org/10.1088/1742-6596/2658/1/012015.

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Abstract Facing the Distributed PV generation inspection scenario, to overcome the low efficiency of traditional manual inspection and the insufficient endurance of existing UAV inspection, UAV aerial inspection and UGVs to provide energy supply in the road network are adopted, and a path planning method that prioritizes the meeting points is proposed. Firstly, the improved genetic algorithm is used to plan the inspection path of each UAV, then the UAV path is clustered to determine the optimal number of meeting points, then the adaptive particle swarm algorithm is used to find the best locati

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Mei, Haoran, and Limei Peng. "Energy-Efficient UAV Trajectory Planning based on Flexible Segment Clustering Algorithm." Journal of Networking and Network Applications 3, no. 3 (2023): 109–18. http://dx.doi.org/10.33969/j-nana.2023.030302.

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This paper plans the energy-efficient UAV trajectory when a UAV gathers data from massive IoT devices in a given area. The UAV trajectory design is addressed by two steps, i.e., IoT node clustering and UAV flight path planning for scanning the clusters, which are formulated as Cluster Minimization (CM) problem and Traveling Salesman Problem (TSP) in this work, respectively. The CM aims to contribute fewest clusters with minimal overlap to cover all the IoT devices and the per cluster size approaching the UAV communication coverage. On the other hand, the TSP seeks to design the shortest flight

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Miao, Fahui, Hangyu Li, and Xiaojun Mei. "Three-Dimensional Path Planning of UAVs for Offshore Rescue Based on a Modified Coati Optimization Algorithm." Journal of Marine Science and Engineering 12, no. 9 (2024): 1676. http://dx.doi.org/10.3390/jmse12091676.

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Unmanned aerial vehicles (UAVs) provide efficient and flexible means for maritime emergency rescue, with path planning being a critical technology in this context. Most existing unmanned device research focuses on land-based path planning in two-dimensional planes, which fails to fully leverage the aerial advantages of UAVs and does not accurately describe offshore environments. Therefore, this paper establishes a three-dimensional offshore environmental model. The UAV’s path in this environment is achieved through a novel swarm intelligence algorithm, which is based on the coati optimization

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Liu, Xiukang, Fufu Wang, Yu Liu, and Long Li. "A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning." Drones 9, no. 2 (2025): 118. https://doi.org/10.3390/drones9020118.

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In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimizati

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Chen, Fankai, Qinyu Liu, Xiaohan Cong, Xiuhuan Dong, and Yuanyuan Zhang. "Three-dimensional path planning of UAV in complex urban environment." Frontiers in Computing and Intelligent Systems 3, no. 2 (2023): 74–77. http://dx.doi.org/10.54097/fcis.v3i2.7514.

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Aiming at the three-dimensional path planning problem of UAV in complex urban environment, the improved grid method is used to simulate the flight environment, and the safety of path planning is improved by building a safe flight area and introducing a navigation safety cost function. In order to solve the problems of A* (A_Star) algorithm in path planning, such as large number of nodes, large amount of computation and low planning efficiency, we can reduce the redundant checking process in the path search process by expanding the line of sight strategy, improve the algorithm search efficiency

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Liu, Fan, Pengchuan Wang, Aniruddha Bhattacharjya, and Qianmu Li. "A Novel Spherical Shortest Path Planning Method for UAVs." Drones 8, no. 12 (2024): 749. https://doi.org/10.3390/drones8120749.

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As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on t

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Gao, Jianfeng, Yu Zheng, Kai Ni, Qiliang Mei, Bin Hao, and Long Zheng. "Fast Path Planning for Firefighting UAV Based on A-Star algorithm." Journal of Physics: Conference Series 2029, no. 1 (2021): 012103. http://dx.doi.org/10.1088/1742-6596/2029/1/012103.

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Abstract When the long distance oil-gas pipeline accident occurs, the fire UAV can give priority to the place where the accident occurs. Emergency investigation and fire rescue, greatly reduces the harm of the accident. However, due to the limitation of the UAV navigation system, the UAV will accumulate the positioning errors over time in the flight process. If the positioning errors can not be corrected in time, it will make the UAV unable to reach the intended destination, thus leading to the failure of the rescue mission. In view of this phenomenon, we propose a UAV path planning scheme con

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Zhang, Jiawei. "Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios." Applied and Computational Engineering 9, no. 1 (2023): 148–53. http://dx.doi.org/10.54254/2755-2721/9/20230068.

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Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning prob

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Zhang, Jian, and Hailong Huang. "Occlusion-Aware UAV Path Planning for Reconnaissance and Surveillance." Drones 5, no. 3 (2021): 98. http://dx.doi.org/10.3390/drones5030098.

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Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc. Their advantage is unprecedented and irreplaceable, especially in environments dangerous to humans, for example, in radiation or pollution-exposed areas. Two path-planning algorithms for reconnaissance and surveillance are proposed in this paper, which ensures every point on the target ground area can be seen at least once in a complete survei

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