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Journal articles on the topic 'Unmanned aerial vehicle path planning'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Dhulkefl, Elaf Jirjees, and Akif Durdu. "Path Planning Algorithms for Unmanned Aerial Vehicles." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 359–62. http://dx.doi.org/10.31142/ijtsrd23696.

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Guan, Wenkai. "Path planning method for unmanned aerial vehicles." Applied and Computational Engineering 33, no. 1 (2024): 266–71. http://dx.doi.org/10.54254/2755-2721/33/20230279.

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Unmanned aerial vehicles, commonly known as drones, have seen tremendous growth and wide-spread use in the last ten years, largely because of unheard-of technological improvements. It is currently one of the most significant study subjects since it involves many different areas of robotics and control systems. This study aims to thoroughly assess the most popular approaches for unmanned aerial vehicle path planning, while also outlining their benefits, drawbacks, potential uses, and overall effectiveness. The objective is to present the academic community with a summary of the existing environ

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Wang, Bo Hang, Dao Bo Wang, and Zain Anwar Ali. "A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method." Measurement and Control 53, no. 1-2 (2020): 83–92. http://dx.doi.org/10.1177/0020294019885155.

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To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of cont

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Yan, Fei, Xiaoping Zhu, Zhou Zhou, and Yang Tang. "Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 13 (2019): 4735–49. http://dx.doi.org/10.1177/0954410019829368.

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The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pytha

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Jiang, Lingzhi, Qiwu Wu, Weicong Tan, Tao Tong, and Weiyi Zhang. "Research on Unmanned Aerial Vehicle Path Planning." Frontiers in Computing and Intelligent Systems 8, no. 3 (2024): 22–24. http://dx.doi.org/10.54097/mnyqs087.

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This paper reviews and analyses the research progress in the field of UAV path planning. Firstly, the importance of UAV path planning and the current research work related to UAV path planning are introduced. Then how UAV path planning is modelled is analysed and key issues to be considered are given. Finally, classical search algorithms, evolutionary algorithms, heuristic search-based algorithms and deep learning methods are analysed in UAV path planning. For each method, its principle, characteristics, advantages and disadvantages, and applicable scenarios are analysed. The aim of this paper

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Xiao-Ying Wu, Xiao-Ying Wu, Xin-Qian Fan Xiao-Ying Wu, Bing-Yan Wei Xin-Qian Fan, and Qian-Han Zhang Bing-Yan Wei. "A Path Planning Method for Logistics Oriented Drone Flight Routes." 電腦學刊 34, no. 5 (2023): 179–87. http://dx.doi.org/10.53106/199115992023103405013.

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<p>This article mainly studies the path planning of unmanned aerial vehicle logistics delivery, considering the constraints in the process of unmanned aerial vehicle delivery, and establishes a unmanned aerial vehicle flight environment model based on logistics management. Based on the performance constraints and task requirements of logistics drones, a multi constraint logistics drone path planning model is established from the perspectives of transportation safety, economy, and speed. The established constraints include flight altitude, maximum angle constraints, energy consumption con

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7

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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Luo, Junhai, Yuxin Tian, and Zhiyan Wang. "Research on Unmanned Aerial Vehicle Path Planning." Drones 8, no. 2 (2024): 51. http://dx.doi.org/10.3390/drones8020051.

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As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aim

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Ibrahim, Nurul Saliha Amani, and Faiz Asraf Saparudin. "Review on path planning algorithm for unmanned aerial vehicles." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 1017. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp1017-1026.

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The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several

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Ibrahim, Nurul Saliha Amani, and Faiz Asraf Saparudin. "Review on path planning algorithm for unmanned aerial vehicles." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 1017–26. https://doi.org/10.11591/ijeecs.v24.i2.pp1017-1026.

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The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several

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11

Sababha, Belal H., Amjed Al-mousa, Remah Baniyounisse, and Jawad Bdour. "Sampling-based unmanned aerial vehicle air traffic integration, path planning, and collision avoidance." International Journal of Advanced Robotic Systems 19, no. 2 (2022): 172988062210864. http://dx.doi.org/10.1177/17298806221086431.

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Unmanned aircraft or drones as they are sometimes called are continuing to become part of more real-life applications. The integration of unmanned aerial vehicles in public airspace is becoming an important issue that should be addressed. As the number of unmanned aerial vehicles and their applications are largely increasing, air traffic with more unmanned aircraft has to be given more attention to prevent collisions and maintain safe skies. Unmanned aerial vehicle air traffic integration and regulation has become a priority to different regulatory agencies and has become of greater interest f

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12

Hino, Takuma, and Takeshi Tsuchiya. "Heuristic path planning of unmanned aerial vehicle formations." International Journal of Intelligent Unmanned Systems 1, no. 2 (2013): 121–44. http://dx.doi.org/10.1108/20496421311330056.

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13

S., Aicevarya Devi, and Vijayalakshmi C. "Unmanned Aerial Vehicle Path Planning using Bat Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 196–99. https://doi.org/10.35940/ijeat.E9285.069520.

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Unmanned Aerial Vehicles (UAV) was introduced after World War II. In 1980’s UAV consider as important weapon system. Initially UAV needs initial position and target position. In this paper bat algorithm is proposed with mixed objective constraints which helps in directing the UAV. The process is initialized by generating the initial population of bat. Then by updating the population size and generation of bat the fitness value with minimum frequency is found that helps to avoid convergence among UAV. Finally the evaluation which gives minimum frequency is considered as optimal solution.

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Choi, Dae H., Byoung H. Jung, and Dan K. Sung. "Energy-aware path planning of an unmanned aerial vehicle acting as a communication relay for mobile ground nodes." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 3 (2018): 1124–32. http://dx.doi.org/10.1177/0954410017748685.

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We focus on energy-aware path planning of a small unmanned aerial vehicle-based relay which forwards the data received from a stationary remote station to a mobile access point. In order to reduce the communication power required for reliable communication between the unmanned aerial vehicle-based relay and access point, the unmanned aerial vehicle-based relay keeps track of the access point within a maximum allowable horizontal distance at a constant altitude, and thus the communication power of the unmanned aerial vehicle-based relay decreases as the horizontal distance decreases. In order t

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Stella, I. Orakwue, and O. Nwazor Nkolika. "Intelligence Gathering Modelling via Unmanned Aerial Vehicle." European Journal of Advances in Engineering and Technology 9, no. 1 (2022): 13–19. https://doi.org/10.5281/zenodo.10643374.

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<strong>ABSTRACT</strong> <em>Unmanned aerial vehicle- (UAV) guided with path planning algorithms can effectively monitor, gather and record intelligent data needed for intelligence gathering. In this work, an advanced and smart type of UAV that is embedded with sensors, wireless links and was considered for deployment, to provide intelligent surveillance for tackling security challenges. To achieve this, a region of interest was chosen and mapped using Google earth pro and quantum geographic information system- (QGIS) software. A model was formulated to determine the number of the ground cont

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16

Zhang, Zhibo. "A review of unmanned aerial vehicle path planning techniques." Applied and Computational Engineering 33, no. 1 (2024): 234–41. http://dx.doi.org/10.54254/2755-2721/33/20230275.

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The paper offers an exhaustive scholarly review of the algorithms and techniques employed in Unmanned Aerial Vehicle (UAV) path planning, categorizing these methodologies based on spatial dimensions, planning steps, and the nature of planning maps. It provides a critical evaluation of a plethora of algorithms, including random search methods, particle swarm algorithms, genetic algorithms, and A* algorithms, among others. The study elucidates the advantages and limitations of each algorithm, with a particular focus on their efficacy in real-time planning and navigation within complex three-dime

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Yu, Guolin, Hui Song, and Jie Gao. "UNMANNED AERIAL VEHICLE PATH PLANNING BASED ON TLBO ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 1310–25. http://dx.doi.org/10.21307/ijssis-2017-707.

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18

Al-Mousa, Amjed, Belal H. Sababha, Nailah Al-Madi, Amro Barghouthi, and Remah Younisse. "UTSim: A framework and simulator for UAV air traffic integration, control, and communication." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141987093. http://dx.doi.org/10.1177/1729881419870937.

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The interest in unmanned systems especially unmanned aerial vehicle is continuously increasing. Unmanned aerial vehicles started to become of great benefit in many different fields. It is anticipated that unmanned aerial vehicles will soon become a main component of the future urban air traffic. The integration of unmanned aerial vehicles within existing air traffic environments has started getting the attention of researchers. Integrating unmanned systems in the real-world urban air traffic requires the development of tools and simulators to enable researchers in their ongoing efforts. In thi

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19

Cravioto, Oleg, Belem Saldivar, Manuel Jiménez-Lizárraga, Juan Carlos Ávila-Vilchis, and Carlos Aguilar-Ibañez. "Sliding Surface-Based Path Planning for Unmanned Aerial Vehicle Aerobatics." Mathematics 12, no. 7 (2024): 1047. http://dx.doi.org/10.3390/math12071047.

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This paper exploits the concept of nonlinear sliding surfaces to be used as a basis in the development of aerial path planning projects involving aerobatic three-dimensional path curves in the presence of disturbances. This approach can be used for any kind of unmanned aerial vehicle aimed at performing aerobatic maneuvers. Each maneuver is associated with a nonlinear surface on which an aerial vehicle could be driven to slide. The surface design exploits the properties of Viviani’s curve and the Hopf bifurcation. A vector form of the super twisting algorithm steers the vehicle to the prescrib

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20

Bal, Mert. "An overview of path planning technologies for unmanned aerial vehicles." Thermal Science 26, no. 4 Part A (2022): 2865–76. http://dx.doi.org/10.2298/tsci2204865b.

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Unmanned aerial vehicles, due to their superior maneuverability and reduced costs can easily perform tasks that are too difficult and complex to be performed with manned aircraft, under all conditions. In order to cope with various obstacles and operate in complex and unstable environmental conditions, the unmanned aerial vehicles must first plan its path. One of the most important problems to investigated in order to find an optimal path between the starting point and the target point of the unmanned aerial vehicles is path planning and choosing the appropriate algorithm. These algorithms fin

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21

Zhao, Xiaolin, Yu Zhang, and Boxin Zhao. "Robust path planning for avoiding obstacles using time-environment dynamic map." Measurement and Control 53, no. 1-2 (2019): 214–21. http://dx.doi.org/10.1177/0020294019847704.

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Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algor

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Yao, Min, and Min Zhao. "Unmanned aerial vehicle dynamic path planning in an uncertain environment." Robotica 33, no. 3 (2014): 611–21. http://dx.doi.org/10.1017/s0263574714000514.

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SUMMARYAn unmanned aerial vehicle (UAV) dynamic path planning method is proposed to avoid not only static threats but also mobile threats. The path of a UAV is planned or modified by the potential trajectory of the mobile threat, which is predicted by its current position, velocity, and direction angle, because the positions of the UAV and mobile threat are dynamically changing. In each UAV planning path, the UAV incurs some costs, including control costs to change the direction angle, route costs to bypass the threats, and threat costs to acquire some probability to be destroyed by threats. T

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Cons, Matthew S., Tal Shima, and Carmel Domshlak. "Integrating Task and Motion Planning for Unmanned Aerial Vehicles." Unmanned Systems 02, no. 01 (2014): 19–38. http://dx.doi.org/10.1142/s2301385014500022.

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This paper investigates the problem where a fixed-winged unmanned aerial vehicle is required to find the shortest flyable path to traverse over multiple targets. The unmanned aerial vehicle is modeled as a Dubins vehicle: a vehicle with a minimum turn radius and the inability to go backward. This problem is called the Dubins traveling salesman problem, an extension of the well-known traveling salesman problem. We propose and compare different algorithms that integrate the task planning and the motion planning aspects of the problem, rather than treating the two separately. An upper bound on ca

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Arokiasami, Willson Amalraj, Prahlad Vadakkepat, Kay Chen Tan, and Dipti Srinivasan. "Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework." Unmanned Systems 06, no. 04 (2018): 231–50. http://dx.doi.org/10.1142/s2301385018500061.

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Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service

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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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Chen, Yang, Jianda Han, and Xingang Zhao. "Three-dimensional path planning for unmanned aerial vehicle based on linear programming." Robotica 30, no. 5 (2011): 773–81. http://dx.doi.org/10.1017/s0263574711000993.

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SUMMARYIn this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. Th

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Elaf, Jirjees Dhulkefl, and Durdu Akif. "Path Planning Algorithms for Unmanned Aerial Vehicles." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 359–62. https://doi.org/10.31142/ijtsrd23696.

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In this paper, the shortest path for Unmanned Aerial Vehicles UAVs is calculated with two dimensional 2D path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment. The aim of this paper is to avoid obstacles and to find the shortest way to the target point. Th e simulation environment was created to evaluate the arrival time on the path planning algorithms A and Dijkstra algorithms for the UAVs. As a result, real time tests were performed with UAVs Elaf Jirjees Dhulkefl | Akif Durdu "Path Planning A

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Chen, Yang, Shiwen Ren, Zhihuan Chen, Mengqing Chen, and Huaiyu Wu. "Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots." Robotica 38, no. 3 (2019): 493–511. http://dx.doi.org/10.1017/s0263574719000808.

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SummaryThis paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This wo

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COSAR, Mustafa. "Path Planning via Swarm Intelligence Algorithms in Unmanned Aerial Vehicle Population." Eurasia Proceedings of Science Technology Engineering and Mathematics 26 (December 30, 2023): 439–50. http://dx.doi.org/10.55549/epstem.1411059.

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Unmanned Aerial Vehicle (UAV) is an autonomous aerial vehicle capable of operating autonomously or in swarm cooperation, performing various tasks in civilian and military domains that exceed human capabilities. These vehicles, which can be produced in different models with varying hardware and software features, include flight control systems, route tracking systems, sensors, and numerous additional components. UAVs have the ability to process data from themselves, the control center, and the external environment. Data processing enables functions such as flight management, swarm optimization,

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Villaseñor, Carlos, Alberto A. Gallegos, Gehova Lopez-Gonzalez, Javier Gomez-Avila, Jesus Hernandez-Barragan, and Nancy Arana-Daniel. "Ellipsoidal Path Planning for Unmanned Aerial Vehicles." Applied Sciences 11, no. 17 (2021): 7997. http://dx.doi.org/10.3390/app11177997.

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The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipso

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Dobrokhodov, Vladimir. "Cooperative Path Planning of Unmanned Aerial Vehicles." Journal of Guidance, Control, and Dynamics 34, no. 5 (2011): 1601–2. http://dx.doi.org/10.2514/1.54851.

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Zhang, Bin, Liang Tang, and Michael Roemer. "Probabilistic Weather Forecasting Analysis for Unmanned Aerial Vehicle Path Planning." Journal of Guidance, Control, and Dynamics 37, no. 1 (2014): 309–12. http://dx.doi.org/10.2514/1.61651.

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Liu, Chun, Shuhang Zhang, and Akram Akbar. "Ground Feature Oriented Path Planning for Unmanned Aerial Vehicle Mapping." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 4 (2019): 1175–87. http://dx.doi.org/10.1109/jstars.2019.2899369.

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Zhang, Sen, Yongquan Zhou, Zhiming Li, and Wei Pan. "Grey wolf optimizer for unmanned combat aerial vehicle path planning." Advances in Engineering Software 99 (September 2016): 121–36. http://dx.doi.org/10.1016/j.advengsoft.2016.05.015.

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田, 茂祥. "Unmanned Aerial Vehicle Path Planning with Improved Ant Colony Algorithm." Computer Science and Application 10, no. 10 (2020): 1900–1907. http://dx.doi.org/10.12677/csa.2020.1010200.

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Li, Xudong, Yang Chen, Zhihuan Chen, and Zixin Huang. "Coverage path planning of bridge inspection with Unmanned aerial vehicle." Engineering Applications of Artificial Intelligence 156 (September 2025): 111253. https://doi.org/10.1016/j.engappai.2025.111253.

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Ma, Yinsong, and Guangcai Liu. "Automated Path Planning for unmanned aerial vehicle in Urban Dynamic Area." Journal of Physics: Conference Series 2252, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1742-6596/2252/1/012045.

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Abstract This paper addresses the path planning and autonomous obstacle avoidance problem of UAVs in urban dynamic area. A flight path planning strategy for UAVs in complex urban environments is proposed.First, the A* algorithm is used to construct a desired global path in a 3D static environment, which is used as the static reference path for dynamic obstacle avoidance below.The environmental and the key points of algorithm are also elaborated. In this paper, the dynamic obstacles are divided into three categories, then, in order to avoid the collision between dynamic obstacles and static opt

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Ma, Yinsong, and Guangcai Liu. "Automated Path Planning for unmanned aerial vehicle in Urban Dynamic Area." Journal of Physics: Conference Series 2252, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1742-6596/2252/1/012045.

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Abstract This paper addresses the path planning and autonomous obstacle avoidance problem of UAVs in urban dynamic area. A flight path planning strategy for UAVs in complex urban environments is proposed.First, the A* algorithm is used to construct a desired global path in a 3D static environment, which is used as the static reference path for dynamic obstacle avoidance below.The environmental and the key points of algorithm are also elaborated. In this paper, the dynamic obstacles are divided into three categories, then, in order to avoid the collision between dynamic obstacles and static opt

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Li, Qing, Gaochen Min, Peng Chen, et al. "Computer vision-based techniques and path planning strategy in a slope monitoring system using unmanned aerial vehicle." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142090430. http://dx.doi.org/10.1177/1729881420904303.

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Unmanned aerial vehicle is a typical field robot which can work in many unstructured environments like mines, forests, and even radiation areas. In our mine monitoring system built in a northeast province of China, special designed unmanned aerial vehicle is applied to take photos and perceive the environment. We select a series of image-based techniques to process aerial pictures to monitor the slope. The visual features are initially refined by histogram equalization. Then, the rocks and cracks can be detected by different digital image processing operators, like Canny, so as to assess displ

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Cicibas, Halil, Kadir Alpaslan Demir, and Nafiz Arica. "Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles." Defence Science Journal 66, no. 6 (2016): 651. http://dx.doi.org/10.14429/dsj.66.9575.

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<p>This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish

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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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Zhang, Jing, Jiwu Li, Hongwei Yang, Xin Feng, and Geng Sun. "Complex Environment Path Planning for Unmanned Aerial Vehicles." Sensors 21, no. 15 (2021): 5250. http://dx.doi.org/10.3390/s21155250.

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Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved wi

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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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Xu, Xing, Feifan Zhang, and Yun Zhao. "Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm." Electronics 12, no. 22 (2023): 4576. http://dx.doi.org/10.3390/electronics12224576.

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This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvemen

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RAJA, M., and Ugur GUVEN. "Design of Obstacle Avoidance and Waypoint Navigation using Global position sensor/ Ultrasonic sensor." INCAS BULLETIN 13, no. 1 (2021): 149–58. http://dx.doi.org/10.13111/2066-8201.2021.13.1.16.

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The objective of the research work to focus on a path planning aims to plan the route of an Unmanned Vehicle (UV) using Global Positioning System (GPS), and the most suitable path is selected avoiding the obstacles along the desired path. The coordinates of the starting point and destination are fed through programming. In addition, an obstacle avoidance algorithm is used and waypoints are given using the AVR Programming. Waypoint Navigation System for the Unmanned Ground Vehicle is use with GPS avoiding the obstacles in its path. The Waypoint Navigation System is the planning of the path of a

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Xie, Ronglei, Zhijun Meng, Yaoming Zhou, Yunpeng Ma, and Zhe Wu. "Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle." Science Progress 103, no. 1 (2019): 003685041987902. http://dx.doi.org/10.1177/0036850419879024.

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In order to solve the problem that the existing reinforcement learning algorithm is difficult to converge due to the excessive state space of the three-dimensional path planning of the unmanned aerial vehicle, this article proposes a reinforcement learning algorithm based on the heuristic function and the maximum average reward value of the experience replay mechanism. The knowledge of track performance is introduced to construct heuristic function to guide the unmanned aerial vehicles’ action selection and reduce the useless exploration. Experience replay mechanism based on maximum average re

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Qi, Yongqiang, Shuai Li, and Yi Ke. "Three-Dimensional Path Planning of Constant Thrust Unmanned Aerial Vehicle Based on Artificial Fluid Method." Discrete Dynamics in Nature and Society 2020 (May 31, 2020): 1–13. http://dx.doi.org/10.1155/2020/4269193.

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In this paper, a three-dimensional path planning problem of an unmanned aerial vehicle under constant thrust is studied based on the artificial fluid method. The effect of obstacles on the original fluid field is quantified by the perturbation matrix, the streamlines can be regarded as the planned path for the unmanned aerial vehicle, and the tangential vector and the disturbance matrix of the artificial fluid method are improved. In particular, this paper addresses a novel algorithm of constant thrust fitting which is proposed through the impulse compensation, and then the constant thrust swi

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Hayajneh, Mohammad R., Mohammad H. Garibeh, Ahmad Bani Younes, and Matthew A. Garratt. "Unmanned Aerial Vehicle Path Planning Using Acceleration-Based Potential Field Methods." Electronics 14, no. 1 (2025): 176. https://doi.org/10.3390/electronics14010176.

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Online path planning for UAVs that are following a moving target is a critical component in applications that demand a soft landing over the target. In highly dynamic situations with accelerating targets, the classical potential field (PF) method, which considers only the relative positions and/or velocities, cannot provide precision tracking and landing. Therefore, this work presents an improved acceleration-based potential field (ABPF) path planning method. This approach incorporates the relative accelerations of the UAV and the target in constructing an attractive field. By controlling the

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Li, Siqi, and Yimin Deng. "Quantum-entanglement pigeon-inspired optimization for unmanned aerial vehicle path planning." Aircraft Engineering and Aerospace Technology 91, no. 1 (2018): 171–81. http://dx.doi.org/10.1108/aeat-03-2018-0107.

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Purpose The purpose of this paper is to propose a new algorithm for independent navigation of unmanned aerial vehicle path planning with fast and stable performance, which is based on pigeon-inspired optimization (PIO) and quantum entanglement (QE) theory. Design/methodology/approach A biomimetic swarm intelligent optimization of PIO is inspired by the natural behavior of homing pigeons. In this paper, the model of QEPIO is devised according to the merging optimization of basic PIO algorithm and dynamics of QE in a two-qubit XXZ Heisenberg System. Findings Comparative experimental results with

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Cai, Fake, Danyang Liu, Weihan Yuan, Shuo Ding, Yongxu Ning, and Chenyang Yue. "Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm." Journal of Physics: Conference Series 2283, no. 1 (2022): 012017. http://dx.doi.org/10.1088/1742-6596/2283/1/012017.

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Abstract In the study of route planning problems in complex environments, in order to reduce the flight cost of unmanned aerial vehicles (UAVs), it is necessary to achieve a better balance between planning time and path quality. This paper utilizes the Rapid-exploration Random Tree (RRT) algorithm for motion planning of a fixed-wing UAV and a multi-rotor UAV (i.e., a quad-rotor UAV), and gives the origin and destination locations on a 3-D map. By following aerodynamic constraints such as maximum roll angle, flight path angle, and airspeed, a collision-free and flight-friendly path is found thr

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