Relevant bibliographies by topics / The UAV path planning problem

Contents

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

Academic literature on the topic 'The UAV path planning problem'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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

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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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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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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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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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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, 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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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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Dissertations / Theses on the topic "The UAV path planning problem"

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Ait, Saadi Amylia. "Coordination of scout drones (UAVs) in smart-city to serve autonomous vehicles." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG064.

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Le sujet des véhicules aériens sans pilote (VAP) est devenu un domaine d'étude prometteurtant dans la recherche que dans l'industrie. En raison de leur autonomie et de leur efficacitéen vol, les drones sont considérablement utilisés dans diverses applications pour différentestâches. Actuellement, l'autonomie du drone est un problème difficile qui peut avoir un impactà la fois sur ses performances et sur sa sécurité pendant la mission. Pendant le vol, les dronesautonomes sont tenus d'investiguer la zone et de déterminer efficacement leur trajectoire enpréservant leurs ressources (énergie liée à
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MARDANI, AFSHIN. "Communication-Aware UAV Path Planning." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2796755.

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Joseph, Jose. "UAV Path Planning with Communication Constraints." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563872872304696.

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Root, Philip J. "Collaborative UAV path planning with deceptive strategies." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32432.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.<br>Includes bibliographical references (p. 87-89).<br>In this thesis, we propose a strategy for a team of Unmanned Aerial Vehicles (UAVs) to perform reconnaissance of an intended route while operating within aural and visual detection range of threat forces. The advent of Small UAVSs (SUAVs) has fundamentally changed the interaction between the observer and the observed. SUAVs fly at much lower altitudes than their predecessors, and the threat can detect the reconnaissance and react to it. This d
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Kamrani, Farzad. "Using on-line simulation in UAV path planning." Licentiate thesis, KTH, Electronic, Computer and Software Systems, ECS, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4529.

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<p>In this thesis, we investigate the problem of Unmanned Aerial Vehicle (UAV) path planning in search or surveillance mission, when some a priori information about the targets and the environment is available. A search operation that utilizes the available a priori information about the initial location of the targets, terrain data, and information from reasonable assumptions about the targets movement can in average perform better than a uniform search that does not incorporate this information. This thesis provides a simulation-based framework to address this type of problem. Search operati
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Stalmakou, Artsiom. "UAV/UAS path planning for ice management information gathering." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13232.

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The key objective of this work is the proposition of the path planning strategy for unmanned aerial vehicle (UAV) intended for information gathering in Arctic environments / ice-infested regions. Two di&#64256;erent path planning strategies are considered; one for analysis of the surveillance area de&#64257;ned as a grid and the other for analysis of the surveillance area de&#64257;ned as a sector. The mixed integer linear programming (MILP), YALMIP modeling language and GUROBI optimizer are used for formulation and solution of the path planning optimization problems. Furthermore, both path pl
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Grimsland, Lars Arne. "UAV Path Planning for Ice Intelligence Purposes using NLP." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18443.

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As the oil and shipping industry are interested in operating in arctic waters, the need for ice intelligence gathering is rising.The thesis describes the implentation and results of a path planning framework for an UAV used for ice intelligence purposes. THe framework produsces paths based on optimization of a non-linear problem, using the IPOPT library in C++.A model for information uncertainty is implemented, and optimal paths based on minimizing the total information uncertainty are compared to optimal paths based on minimizing distance between UAV and target. Both off line and offline path
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Caves, Américo De Jesús (Caves Corral). "Human-automation collaborative RRT for UAV mission path planning." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61145.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 105-111).<br>Future envisioned Unmanned Aerial Vehicle (UAV) missions will be carried out in dynamic and complex environments. Human-automation collaboration will be required in order to distribute the increased mission workload that will naturally arise from these interactions. One of the areas of interest in these missions is the supervision of multiple UAVs by a single operator, and it is crit
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Lin, Rongbin Lanny. "UAV intelligent path planning for wilderness search and rescue /." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2906.pdf.

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Lechliter, Matthew C. "Decentralized control for UAV path planning and task allocation." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=3314.

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Thesis (M.S.)--West Virginia University, 2004.<br>Title from document title page. Document formatted into pages; contains x, 198 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 134-138).
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Books on the topic "The UAV path planning problem"

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Kindl, Mark Richard. A stochastic approach to path planning in the Weighted-Region Problem. Naval Postgraduate School, 1991.

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Pearpoint, Jack. PATH: A workbook for planning positive possible futures : planning alternative tomorrows with hope : for schools, organizations, businesses, families. 2nd ed. Inclusion Press, 1993.

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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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Heuristic Approach to the Path Planning Problem in a Raster Map. Diane Pub Co, 1994.

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Book chapters on the topic "The UAV path planning problem"

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Xu, Jiankang, Xuzhou Shi, Zesheng Zhu, and Hang Gao. "Survey on UAV Coverage Path Planning Problem." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_211.

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Golabi, Mahmoud, Soheila Ghambari, Shilan Amir Ashayeri, Laetitia Jourdan, and Lhassane Idoumghar. "A Multi-objective 3D Offline UAV Path Planning Problem with Variable Flying Altitude." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42616-2_14.

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Alihodzic, Adis, Damir Hasic, and Elmedin Selmanovic. "An Effective Guided Fireworks Algorithm for Solving UCAV Path Planning Problem." In Numerical Methods and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10692-8_3.

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Pan, Jeng-Shyang, Jenn-Long Liu, and En-Jui Liu. "Improved Whale Optimization Algorithm and Its Application to UCAV Path Planning Problem." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5841-8_5.

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Debnath, Dipraj, and A. F. Hawary. "Adapting Travelling Salesmen Problem for Real-Time UAS Path Planning Using Genetic Algorithm." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0866-7_12.

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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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Nikolos, Ioannis K., Eleftherios S. Zografos, and Athina N. Brintaki. "UAV Path Planning Using Evolutionary Algorithms." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72696-8_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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Chae, Hyeok-Joo, Soon-Seo Park, Han-Vit Kim, Hyo-Sang Ko, and Han-Lim Choi. "UAV Path Planning for Local Defense Systems." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8323-6_17.

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Zhang, Xiaodong, Xiangyang Hao, Guopeng Sun, and Yali Xu. "Obstacle Avoidance Path Planning of Rotor UAV." In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4588-2_41.

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Conference papers on the topic "The UAV path planning problem"

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Li, Xiaokang, Xuzhao Chai, GuanHao Zhou, et al. "Improved ant colony algorithm with Q-learning adaptation mechanism for coverage UAV path planning in search problem." In 4th International Conference on Internet of Things and Smart City, edited by Xinwei Yao and Francisco Falcone. SPIE, 2024. http://dx.doi.org/10.1117/12.3035140.

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Lakhmani, Sagar, Jack Langelaan, and Alan Wagner. "Human-intuitable Collision Avoidance for Autonomous and Semi-Autonomous Aerial Vehicles." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12921.

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This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other m
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Zhou, Dianyi, Xi Du, Shiyi Liu, Qingyu Su, and Hongyang Guo. "Research on LSTM-driven UAV path planning." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049651.

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Yan, Qiang, Qi Zhao, and Ye Zhang. "Multi-UAV Path Planning Based on MAA2C." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10699006.

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Liu, Siyuan, Hongyu Fang, Ziyang Deng, and Dingsheng Luo. "Sensorimotor Coordinated Multi-UAV Coverage Path Planning." In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731246.

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Luo, Ziqiang, and Shuhan Fang. "UAV Path Mountain Planning Based on SPSO." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796170.

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Sil, Mitali, Pratyusha Rakshit, Sayan Chatterjee, and Archana Chowdhury. "Coevolutionary Optimization for Multi-UAV Path-Planning." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11043096.

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Radmanesh, Mohammadreza, and Manish Kumar. "UAV Path Planning in the Framework of MILP-Tropical Optimization." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5231.

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This paper proposes a fast method for obtaining mathematically optimal trajectories for UAVs while avoiding collisions. A comparison of the proposed method with previously used Mixed Integer Linear Programming (MILP) to find the optimal collision-free path UAVs, aircraft, and spacecraft show the effectiveness and performance of this method. Here, the UAV path planning problem is formulated in the new framework named MILP-Tropical optimization that exploits tropical mathematics for obtaining solution and then casted in a novel branch-and-bound method. Various constraints including UAV dynamics
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Tisdale, John, and J. Karl Hedrick. "A UAV Trajectory Planning Algorithm for Simultaneous Search and Track." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-81100.

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This paper considers trajectories for an unmanned aerial vehicle (UAV) that must search an area while tracking a target. The UAV has a constrained turn rate and a constant velocity; it is assumed that there are certain areas of interest that have a higher search value than others. An algorithm is presented that seeks to maximize the value of the area searched while still maintaining the track. The problem is discretized in both time and the control; the motion of the UAV is constrained to the reachability graph, a subset of the forward reachable set. At each revisit, the target path is estimat
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Ghambari, Soheila, Lhassane Idoumghar, Laetitia Jourdan, and Julien Lepagnot. "An Improved TLBO Algorithm for Solving UAV Path Planning Problem." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9003160.

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Reports on the topic "The UAV path planning problem"

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Rokita, Dagmar, Rainer Sawatzki, and Raushan Szyzdykova. Energy Transition in Central Asia: a Short Review. Kazakh German University, 2022. http://dx.doi.org/10.29258/dkucrswp/2022/20-52.eng.

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The five countries of Central Asia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan and Turkmenistan, have each adopted climate targets to achieve the climate goals agreed in Paris by 2050. In this paper, the starting positions of all five countries are presented and the respective obstacles on the path to climate neutrality are identified. The starting positions in the countries with large oil, gas or coal reserves (Kazakhstan, Uzbekistan and Turkmenistan) differ from the countries where the basis of energy supply are large hydroelectric plants (Kyrgyzstan and Tajikistan). One problem in all c
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Buesseler, Buessele, Daniele Bianchi, Fei Chai, et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, 2023. http://dx.doi.org/10.1575/1912/67120.

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We need a new way of talking about global warming. UN Secretary General António Guterres underscored this when he said the “era of global boiling” has arrived. Although we have made remarkable progress on a very complex problem over the past thirty years, we have a long way to go before we can keep the global temperature increase to below 2°C relative to the pre-industrial times. Climate models suggest that this next decade is critical if we are to avert the worst consequences of climate change. The world must continue to reduce greenhouse gas emissions, and find ways to adapt and build resili
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