Relevant bibliographies by topics / Unmanned aerial vehicle path planning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Unmanned aerial vehicle path planning'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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

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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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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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Dissertations / Theses on the topic "Unmanned aerial vehicle path planning"

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Griner, Alina. "Human-RRT collaboration in Unmanned Aerial Vehicle mission path planning." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76913.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 147-149).<br>Unmanned Aerial Vehicles (UAVs) are used for a variety of military and commercial purposes, including surveillance, combat, and search and rescue. Current research is looking into combining automation with human supervision to facilitate various lower-level cognitive tasks, such as path planning, in order to allow the human operator to focus on high-level mission strategy. P
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Yu, Kevin Li. "Coverage Planning for Unmanned Aerial Vehicles." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103705.

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This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV) for the task of covering an environment. Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying on a fixed altitude plane parallel to the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile r
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Bradley, Justin, and Breton Prall. "AN UNMANNED AERIAL VEHICLE PROJECT FOR UNDERGRADUATES." International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604143.

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ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California<br>Brigham Young University recently introduced a project for undergraduates in which a miniature unmanned aerial vehicle system is constructed. The system is capable of autonomous flight, takeoff, landing, and navigation through a planned path. In addition, through the use of video and telemetry collected by the vehicle, accurate geolocation of specified targets is performed. T
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Mize, Lloyd B. IV. "Development of a Multiple Vehicle Collaborative Unmanned Aerial System." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2527.

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The purpose of this research was to design a multiple UAV system with collaborative operation. This project is built on work that has been done in the field of Unmanned Systems at VCU and is aimed at providing a starting point for research into collaborative control of multiple UAVs. The current GCS software was extended to include multiple vehicles per single controller via a new communication protocol. Many changes were made to the user interface to facilitate controlling multiple vehicles with a single operator. A second processor, called an MCS, was added to each vehicle to allow for g
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Eriksson, Urban. "Dynamic Path Planning for Autonomous Unmanned Aerial Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241243.

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This thesis project investigates a method for performing dynamic path planning in three dimensions, targeting the application of autonomous unmanned aerial vehicles (UAVs).  Three different path planning algorithms are evaluated, based on the framework of rapidly-exploring random trees (RRTs): the original RRT, RRT*, and a proposed variant called RRT-u, which differs from the two other algorithms by considering dynamic constraints and using piecewise constant accelerations for edges in the planning tree. The path planning is furthermore applied for unexplored environments. In order to select a
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Rennu, Samantha R. "Dynamic Mission Planning for Unmanned Aerial Vehicles." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton16082274381124.

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Fan, Jiankun. "Optimal Path Planning and Control of Quadrotor Unmanned Aerial Vehicle for Area Coverage." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1417345596.

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Noonan, Andrea L. "Flight plan generation for unmanned aerial vehicles." Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/385.

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Nguyen, Joseph Luan. "Long-term Informative Path Planning with Autonomous Soaring." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/15364.

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The ability of UAVs to cover large areas efficiently is valuable for information gathering missions. For long-term information gathering, a UAV may extend its endurance by accessing energy sources present in the atmosphere. Thermals are a favourable source of wind energy and thermal soaring is adopted in this thesis to enable long-term information gathering. This thesis proposes energy-constrained path planning algorithms for a gliding UAV to maximise information gain given a mission time that greatly exceeds the UAV's endurance. This thesis is motivated by the problem of probabilistic target
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Omar, Rosli bin. "Path planning for unmanned aerial vehicles using visibility line-based methods." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10906.

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This thesis concerns the development of path planning algorithms for unmanned aerial vehicles (UAVs) to avoid obstacles in two- (2D) and three-dimensional (3D) urban environments based on the visibility graph (VG) method. As VG uses all nodes (vertices) in the environments, it is computationally expensive. The proposed 2D path planning algorithms, on the contrary, select a relatively smaller number of vertices using the so-called base line (BL), thus they are computationally efficient. The computational efficiency of the proposed algorithms is further improved by limiting the BL’s length, whic
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Books on the topic "Unmanned aerial vehicle path planning"

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Tsourdos, Antonios, Brian White, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974636.

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White, Brian, 1947 June 6- and Shanmugavel Madhavan, eds. Cooperative path planning of unmanned aerial vehicles. Wiley, 2011.

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White, Brian, 1947 June 6-, Shanmugavel Madhavan, and Zhu Xiaoping 1963 September-, eds. Wu ren ji xie tong lu jing gui hua: Cooperative path planning of unmanned aerial vehicles. Guo fang gong ye chu ban she, 2013.

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White, Brian, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. Wiley & Sons, Limited, John, 2010.

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White, Bruce, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. American Institute of Aeronautics & Astronautics, 2010.

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White, Brian, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. Wiley & Sons, Incorporated, John, 2010.

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White, Brian, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. Wiley & Sons, Incorporated, John, 2011.

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White, Brian, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. Wiley & Sons, Incorporated, John, 2010.

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White, Brian, Antonios Tsourdos, and Madhavan Shanmugavel. Cooperative Path Planning of Unmanned Aerial Vehicles. Wiley & Sons, Incorporated, John, 2010.

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Addressing Disasters in Smart Cities Through UAVs Path Planning and 5G Communications: Smart Cities,Disaster Management. Unmanned Aerial Vehicles ,UAV Path Planning,UAV Communication Networks. Independently Published, 2021.

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Book chapters on the topic "Unmanned aerial vehicle path planning"

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De Filippis, Luca, Giorgio Guglieri, and Fulvia Quagliotti. "A Minimum Risk Approach for Path Planning of UAVs." In Unmanned Aerial Vehicles. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-94-007-1110-5_14.

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Duan, Haibin, and Pei Li. "UAV Path Planning." In Bio-inspired Computation in Unmanned Aerial Vehicles. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41196-0_4.

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Valavanis, Kimon P., and George J. Vachtsevanos. "UAV Mission and Path Planning: Introduction." In Handbook of Unmanned Aerial Vehicles. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-90-481-9707-1_143.

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Adolf, Florian-M., and Heiko Hirschmüller. "Meshing and Simplification of High Resolution Urban Surface Data for UAV Path Planning." In Unmanned Aerial Vehicles. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-94-007-1110-5_12.

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Medeiros, Felipe Leonardo Lôbo, and José Demisio Simões da Silva. "Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground." In Unmanned Aerial Vehicles. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-94-007-1110-5_13.

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Moon, Sangwoo, David Hyunchul Shim, and Eunmi Oh. "Cooperative Task Assignment and Path Planning for Multiple UAVs." In Handbook of Unmanned Aerial Vehicles. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-90-481-9707-1_82.

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Oh, Hyondong, Hyo-Sang Shin, Seungkeun Kim, Antonios Tsourdos, and Brian A. White. "Cooperative Mission and Path Planning for a Team of UAVs." In Handbook of Unmanned Aerial Vehicles. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-90-481-9707-1_14.

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Cárdenas R., César A., V. Landero, Ramón E. R. González, Paola Ariza-Colpas, Emiro De-la-Hoz-Franco, and Carlos Andrés Collazos-Morales. "Path Planning Approach for a Quadrotor Unmanned Aerial Vehicle." In Computational Science and Its Applications – ICCSA 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86960-1_30.

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Irani, Behnam, Weidong Chen, and Jingchuan Wang. "A Localizability Constraint-Based Path Planning Method for Unmanned Aerial Vehicle." In Intelligent Autonomous Systems 15. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01370-7_71.

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Zheng, Jun, and Yujie Zhou. "Research on Path Planning Algorithm for Unmanned Aerial Vehicles." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88294-4_20.

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Conference papers on the topic "Unmanned aerial vehicle path planning"

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Pawar, Abhilasha, Shashwat Yadav, Abhavya Gupta, Mohd Aamir, Aakarshan Singh, and Amit Kumar Roy. "Path Planning of Unmanned Aerial Vehicle using Model Predictive Control." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688135.

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Zhao, Xueying, Haolun Zhang, and Yufeng Li. "Unmanned aerial vehicle mountain path planning based on improved genetic algorithm." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039804.

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Jia, Rong, YeXiang Wei, Darong Zhu, Congju Zuo, Weihua Yuan, and Feng Wang. "Overview of path planning algorithms for unmanned aerial vehicles." In International Conference on Pattern Recognition and Image Analysis, edited by Mingguang Shan and Tao Lei. SPIE, 2025. https://doi.org/10.1117/12.3056218.

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Li, Wenguang, Hao Wu, Xinxin Huang, Fengming Shi, Wei Li, and Song Zhao. "Unmanned aerial vehicle path planning method based on football team training algorithm." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695324.

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Li, Shengpeng, and Yuhan Qian. "Unmanned Aerial Vehicle Autonomous Flight Path Planning Algorithm Based on Deep Learning." In 2024 International Conference on Telecommunications and Power Electronics (TELEPE). IEEE, 2024. http://dx.doi.org/10.1109/telepe64216.2024.00119.

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Zhang, Hongyu, Xiaomei Xie, Mingzhu Wei, Xinhua Wang, Dongjing Song, and Jin Luo. "An Improved Goal-bias RRT algorithm for Unmanned Aerial Vehicle Path Planning." In 2024 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2024. http://dx.doi.org/10.1109/icma61710.2024.10633102.

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Sharma, Ashish. "Improved Red-Billed Blue Magpie Optimizer for Unmanned Aerial Vehicle Path Planning." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864415.

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Ma, Xu, Fangkun Zhang, and Baoming Shan. "An Improved A* Algorithm for 3D Path Planning of Unmanned Aerial Vehicle." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865791.

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Li, Zhiqiang, Jingyu Liu, Mengji Shi, Boxian Lin, Meng Li, and Kaiyu Qin. "Adaptive Budgerigar Optimization-Based Obstacle Avoidance Path Planning for Unmanned Aerial Vehicle." In 2024 14th International Conference on Information Science and Technology (ICIST). IEEE, 2024. https://doi.org/10.1109/icist63249.2024.10805303.

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Byeon, Haewon, Pramod Kumar, Kottala Sri Yogi, Leeladhar Gudala, Mukesh Soni, and Samarth Motagi. "Transportation Path Planning Model for Multi-Constraint Unmanned Aerial Vehicle in Healthcare." In 2024 IEEE 2nd International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP). IEEE, 2024. https://doi.org/10.1109/ihcsp63227.2024.10960004.

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Reports on the topic "Unmanned aerial vehicle path planning"

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Ennasr, Osama, Charles Ellison, Anton Netchaev, Ahmet Soylemezoglu, and Garry Glaspell. Unmanned ground vehicle (UGV) path planning in 2.5D and 3D. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47459.

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Herein, we explored path planning in 2.5D and 3D for unmanned ground vehicle (UGV) applications. For real-time 2.5D navigation, we investigated generating 2.5D occupancy grids using either elevation or traversability to determine path costs. Compared to elevation, traversability, which used a layered approach generated from surface normals, was more robust for the tested environments. A layered approached was also used for 3D path planning. While it was possible to use the 3D approach in real time, the time required to generate 3D meshes meant that the only way to effectively path plan was to
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Bodie, Mark, Michael Parker, Alexander Stott, and Bruce Elder. Snow-covered obstacles’ effect on vehicle mobility. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/38839.

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The Mobility in Complex Environments project used unmanned aerial systems (UAS) to identify obstacles and to provide path planning in forward operational locations. The UAS were equipped with remote-sensing devices, such as photogrammetry and lidar, to identify obstacles. The path-planning algorithms incorporated the detected obstacles to then identify the fastest and safest vehicle routes. Future algorithms should incorporate vehicle characteristics as each type of vehicle will perform differently over a given obstacle, resulting in distinctive optimal paths. This study explored the effect of
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Lee, Jin-Kyu, Amir Naser, Osama Ennasr, Ahmet Soylemezoglu,, and Garry Glaspell. Unmanned ground vehicle (UGV) full coverage planning with negative obstacles. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47527.

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We explored approaches that offer full coverage path planning while simultaneously avoiding negative obstacles. These approaches are specific to unmanned ground vehicles (UGVs), which need to constantly interact with a traversable ground surface. We tested multiple potential solutions in simulation, and the results are presented herein. Full coverage path planner (FCPP) approaches were evaluated based on their ability to discretize their paths, use waypoints effectively, and be easily integrated with our current robot platform. For negative obstacles, we explored approaches that will integrate
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Ennasr, Osama, Brandon Dodd, Michael Paquette, Charles Ellison, and Garry Glaspell. Low size, weight, power, and cost (SWaP-C) payload for autonomous navigation and mapping on an unmanned ground vehicle. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47683.

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Autonomous navigation and unknown environment exploration with an unmanned ground vehicle (UGV) is extremely challenging. This report investigates a mapping and exploration solution utilizing low size, weight, power, and cost payloads. The platform presented here leverages simultaneous localization and mapping to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, 3D lidar, and red-green-blue and depth cameras. The main goal of this effort is to leverage path planning and navigation for mapping and explorat
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