Relevant bibliographies by topics / The UAV path planning problem

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

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Published: 25 May 2024

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

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Wei, Zhiqiang, Yu Hu, Zhiyan Dong, Wenbin Bai, Haiyue Yang, Yaen Xie, Feng Shu, and Lihua Zhang. "UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter." Journal of Physics: Conference Series 2755, no. 1 (May 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 the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.
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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 (May 23, 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 configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.
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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 (April 22, 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 network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications
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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 (September 26, 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 the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.
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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 (June 17, 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 problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.
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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 life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.
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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 (September 1, 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 satisfactorily. To address the above problems, this paper proposes a UAV path planning method based on deep reinforcement learning algorithm. Based on the OpenAI-GYM architecture, a 3D map environment model is constructed, with the map grid as the state set and 26 actions as the action set, which does not need an environment model and relies on its own interaction with the environment to complete the path planning task. The algorithm is based on stochastic process theory, modeling the path planning problem as a Markov Decision Process (MDP), fitting the UAV path planning decision function and state-action function, and designing the DQN algorithm model according to the state space, action space and network structure. The algorithm enables the intelligences to carry out strategy iteration efficiently. Through simulation, the DQN algorithm is verified to avoid obstacles and complete the path planning task in only about 160 rounds, which validates the effectiveness of the proposed path planning algorithm.
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Xu, Yiqing, Jiaming Li, and Fuquan Zhang. "A UAV-Based Forest Fire Patrol Path Planning Strategy." Forests 13, no. 11 (November 18, 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-organizing mapping (RSOM) algorithm to plan a corresponding flight path. In addition, since it is difficult for a single UAV to complete a single full-path cruise task in a large area within its maximum cruise time, a multi-UAV cruise scheme is proposed. First, the Gaussian mixture clustering algorithm is used to cluster the study area and divide it into several subareas. In combination with the RSOM algorithm, the corresponding path is planned for each UAV. A simulation with an actual dataset showed that the proposed method solves the problem of UAV patrol path planning for forest fire monitoring and can complete the task within a reasonable time.
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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 obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.
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Wang, Wentao, Chen Ye, and Jun Tian. "SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning." Drones 7, no. 7 (July 7, 2023): 452. http://dx.doi.org/10.3390/drones7070452.

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The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.
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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 la fois à l'altitude et à la longueur de la trajectoire) et en satisfaisant certaines contraintes (obstacles et rotations d'axe). Ce problème estdéfini comme le problème de planification de trajectoire UAV qui nécessite des algorithmesefficaces pour être résolus, souvent des algorithmes d'intelligence artificielle. Dans cettethèse, nous présentons deux nouvelles approches pour résoudre le problème de planificationde trajectoire UAV. La première approche est un algorithme amélioré basé sur l'algorithmed'optimisation des vautours africains, appelé algorithmes CCO-AVOA, qui intègre la cartechaotique, la mutation de Cauchy et les stratégies d'apprentissage basées sur l'oppositiond'élite. Ces trois stratégies améliorent les performances de l'algorithme AVOA original entermes de diversité des solutions et d'équilibre de recherche exploration/exploitation. Unedeuxième approche est une approche hybride, appelée CAOSA, basée sur l'hybridation deChaotic Aquila Optimization avec des algorithmes de recuit simulé. L'introduction de lacarte chaotique améliore la diversité de l'optimisation Aquila (AO), tandis que l'algorithmede recuit simulé (SA) est appliqué comme algorithme de recherche locale pour améliorer larecherche d'exploitation de l'algorithme AO traditionnel. Enfin, l'autonomie et l'efficacitédu drone sont abordées dans une autre application importante, qui est le problème de placement du drone. La question du placement de l'UAV repose sur la recherche de l'emplacementoptimal du drone qui satisfait à la fois la couverture du réseau et la connectivité tout entenant compte de la limitation de l'UAV en termes d'énergie et de charge. Dans ce contexte, nous avons proposé un hybride efficace appelé IMRFO-TS, basé sur la combinaisonde l'amélioration de l'optimisation de la recherche de nourriture des raies manta, qui intègreune stratégie de contrôle tangentiel et d'algorithme de recherche taboue
The subject of Unmanned Aerial Vehicles (UAVs) has become a promising study field in bothresearch and industry. Due to their autonomy and efficiency in flight, UAVs are considerablyused in various applications for different tasks. Actually, the autonomy of the UAVis a challenging issue that can impact both its performance and safety during the mission.During the flight, the autonomous UAVs are required to investigate the area and determineefficiently their trajectory by preserving their resources (energy related to both altitude andpath length) and satisfying some constraints (obstacles and axe rotations). This problem isdefined as the UAV path planning problem that requires efficient algorithms to be solved,often Artificial Intelligence algorithms. In this thesis, we present two novel approachesfor solving the UAV path planning problem. The first approach is an improved algorithmbased on African Vultures Optimization Algorithm (AVOA), called CCO-AVOA algorithms,which integrates the Chaotic map, Cauchy mutation, and Elite Opposition-based learningstrategies. These three strategies improve the performance of the original AVOA algorithmin terms of the diversity of solutions and the exploration/exploitation search balance. Asecond approach is a hybrid-based approach, called CAOSA, based on the hybridization ofChaotic Aquila Optimization with Simulated Annealing algorithms. The introduction of thechaotic map enhances the diversity of the Aquila Optimization (AO), while the SimulatedAnnealing (SA) algorithm is applied as a local search algorithm to improve the exploitationsearch of the traditional AO algorithm. Finally, the autonomy and efficiency of the UAVare tackled in another important application, which is the UAV placement problem. Theissue of the UAV placement relays on finding the optimal UAV placement that satisfies boththe network coverage and connectivity while considering the UAV's limitation from energyand load. In this context, we proposed an efficient hybrid called IMRFO-TS, based on thecombination of Improved Manta Ray Foraging Optimization, which integrates a tangentialcontrol strategy and Tabu Search algorithms
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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.
Includes bibliographical references (p. 87-89).
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 dynamic between the reconnaissance vehicles and the threat observers requires that we view this scenario within a game theoretic framework. We begin by proposing two discrete optimization techniques, a recursive algorithm and a Mixed Integer Linear Programming (MILP) model, that seek a unique optimal trajectory for a team of SUAVs or agents for a given environment. We then develop a set of heuristics governing the agents' optimal strategy or policy within the formalized game, and we use these heuristics to produce a randomized algorithm that outputs a set of waypoints for each vehicle. Finally, we apply this final algorithm to a team of autonomous rotorcraft to demonstrate that our approach operates flawlessly in real-time environments.
by Philip J. Root.
S.M.
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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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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 operations are generally dynamic and should be modified during the mission due to new reports from other sources, new sensor observations, and/or changes in the environment, therefore a Symbiotic Simulation method that employs the latest data is suggested. All available information is continuously fused using Particle Filtering to yield an updated picture of the probability density of the target. This estimation is used periodically to run a set of what-if simulations to determine which UAV path is most promising. From a set of different UAV paths the one that decreases the uncertainty about the location of the target is preferable. Hence, the expectation of information entropy is used as a measure for comparing different courses of action of the UAV. The suggested framework is applied to a test case scenario involving a single UAV searching for a single target moving on a road network. The performance of the Symbiotic Simulation search method is compared with an off-line simulation and an exhaustive search method using a simulation tool developed for this purpose. The off-line simulation differs from the Symbiotic Simulation search method in that in the former case the what-if simulations are conducted before the start of the mission. In the exhaustive search method the UAV searches the entire road network. The Symbiotic Simulation shows a higher performance and detects the target in the considerably shorter time than the other two methods. Furthermore, the detection time of the Symbiotic Simulation is compared with the detection time when the UAV has the exact information about the initial location of the target, its velocity and its path. This value provides a lower bound for the optimal solution and gives another indication about the performance of the Symbiotic Simulation. This comparison also suggests that the Symbiotic Simulation in many cases achieves a “near” optimal performance.

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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 different path planning strategies are considered; one for analysis of the surveillance area defined as a grid and the other for analysis of the surveillance area defined 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 planning strategies are tested for the cases of constant and variable ice flow, respectively; the following are investigated in each simulation case: flight path of the UAV, coverage of the surveillance area, speed and acceleration of the UAV and the solver-time. Moreover, throughout this work only a planar motion is considered, with one single UAV used in each simulation case.
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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 planning is tested with single and multiple targets.It was found that minimizing information uncertainty can work very well for path planning for ice berg surveillance, or for surveillance of a small search grid.Minimizing information uncertainty generally gave better results than minimizing distance between the UAV and given targets.The implementation should be made more robust, and interfaces towards other UAV sysmets has to be made before the path planning platform has any practical use.
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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.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 105-111).
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 critical to understand how individual operators will be able to supervise a team of vehicles performing semi-autonomous path planning while avoiding no-fly zones and replanning on the fly. Unfortunately, real time planning and replanning can be a computationally burdensome task, particularly in the high density obstacle environments that are envisioned in future urban applications. Recent work has proposed the use of a randomized algorithm known as the Rapidly exploring Random Tree (RRT) algorithm for path planning. While capable of finding feasible solutions quickly, it is unclear how well a human operator will be able to supervise a team of UAVs that are planning based on such a randomized algorithm, particularly due to the unpredictable nature of the generated paths. This thesis presents the results of an experiment that tested a modification of the RRT algorithm for use in human supervisory control of UAV missions. The experiment tested how human operators behaved and performed when given different ways of interacting with an RRT to supervise UAV missions in environments with dynamic obstacle fields of different densities. The experimental results demonstrated that some variants of the RRT increase subjective workload, but did not provide conclusive evidence for whether using an RRT algorithm for path planning is better than manual path planning in terms of overall mission times. Analysis of the data and behavioral observations hint at directions for possible future work.
by Americo De Jesus Caves.
M.Eng.
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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.
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. Monterey, Calif: 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. Toronto: Inclusion Press, 1993.

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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, 1601–7. Singapore: 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, 187–200. Cham: 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, 29–38. Cham: 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, 37–47. Singapore: 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, 151–63. Singapore: 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, 99–142. Berlin, Heidelberg: 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, 77–111. Berlin, Heidelberg: 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, 1443–46. Dordrecht: 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, 199–211. Singapore: 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, 473–83. Singapore: 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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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 are incorporated in the proposed Tropical framework and a solution methodology is presented. An extensive numerical study shows that the proposed method provides faster solution. The proposed technique can be extended to distributed control for multiple vehicles and multiple way-points.
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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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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 estimated for the next revisit. A heuristic method is used to determine the best UAV path, because the target path is not known a priori. Feasible paths are found by examining the terminating vertices of the reachability graph. A cooperative implementation, for a team of UAVs patrolling the same region, is developed. Simulation indicates the feasibility of the method for a real-time implementation. Trajectories for example scenarios are presented and discussed.
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Rocha, Lídia, and Kelen Vivaldini. "Comparison between Meta-Heuristic Algorithms for Path Planning." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14950.

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Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.
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Rudnick-Cohen, Eliot, Jeffrey W. Herrmann, and Shapour Azarm. "Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47407.

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Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.
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Zhe Zhang, Jianxun Li, and Jun Wang. "Sequential convex programming for nonlinear optimal control problem in UAV path planning." In 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963240.

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Chen, Jie, Fang Ye, and Yibing Li. "Travelling salesman problem for UAV path planning with two parallel optimization algorithms." In 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL). IEEE, 2017. http://dx.doi.org/10.1109/piers-fall.2017.8293250.

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Li, Mickey, Arthur G. Richards, and Mahesh Sooriyabandara. "Experimental Validation of the Reliability-Aware Multi-UAV Coverage Path Planning Problem." In AIAA SCITECH 2024 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-2879.

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Han, Yingjie, and Wei Gao. "Research on UAV Multi-Objective Path Planning Problem Based on Optimization Algorithm." In 2023 3rd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). IEEE, 2023. http://dx.doi.org/10.1109/cei60616.2023.10527848.

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Chen, Jinchao, Mengyuan Li, Zhenyu Yuan, and Qing Gu. "An Improved A* Algorithm for UAV Path Planning Problems." In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2020. http://dx.doi.org/10.1109/itnec48623.2020.9084806.

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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, July 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 countries is the poorly developed power grid, which is partly outdated and not designed for high throughput rates. Existing power plants are mainly located in metropolitan regions and rural areas are partly undersupplied. If wind and solar power plants are built on a large scale in uninhabited areas, the lack of transmission lines is a major problem. Another problem is that energy prices are sometimes heavily subsidised, which can make it difficult for the population to accept necessary investments in the renewable energy sector. Especially in economically weak sections of the population, resistance to market-based energy prices is likely to be particularly strong. In the long term, information and increased education of large parts of the population can significantly improve the acceptance of the energy transition from carbon-based energy to solar, wind and small hydropower. The use of renewable energy is still in its infancy in all countries and must develop quickly if the ambitious climate goals are to be achieved. To this end, the training of local experts is particularly important. To this end, centres should be established at selected locations where local experts can be trained and further educated in various fields, from conception and planning to construction, maintenance and operation.
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Buesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John, et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, October 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 resilience among vulnerable communities. At the same time, we need to find new ways to remove carbon dioxide from the atmosphere in order to chart a “net negative” emissions pathway. Given their large capacity for carbon storage, the oceans must be included in consideration of our multiple carbon dioxide removal (CDR) options. This report focused on ocean iron fertilization (OIF) for marine CDR. This is by no means a new scientific endeavor. Several members of ExOIS (Exploring Ocean Iron Solutions) have been studying this issue for decades, but the emergence of runaway climate impacts has motivated this group to consider a responsible path forward for marine CDR. That path needs to ensure that future choices are based upon the best science and social considerations required to reduce human suffering and counter economic and ecological losses, while limiting and even reversing the negative impacts that climate change is already having on the ocean and the rest of the planet. Prior studies have confirmed that the addition of small amounts of iron in some parts of the ocean is effective at stimulating phytoplankton growth. Through enhanced photosynthesis, carbon dioxide can not only be removed from the atmosphere but a fraction can also be transferred to durable storage in the deep sea. However, prior studies were not designed to quantify how effective this storage can be, or how wise OIF might be as a marine CDR approach. ExOIS is a consortium that was created in 2022 to consider what OIF studies are needed to answer critical questions about the potential efficiency and ecological impacts of marine CDR (http://oceaniron.org). Owing to concerns surrounding the ethics of marine CDR, ExOIS is organized around a responsible code of conduct that prioritizes activities for the collective benefit of our planet with an emphasis on open and transparent studies that include public engagement. Our goal is to establish open-source conventions for implementing OIF for marine CDR that can be assessed with appropriate monitoring, reporting, and verification (MRV) protocols, going beyond just carbon accounting, to assess ecological and other non-carbon environmental effects (eMRV). As urgent as this is, it will still take 5 to 10 years of intensive work and considerable resources to accomplish this goal. We present here a “Paths Forward’’ report that stems from a week-long workshop held at the Moss Landing Marine Laboratories in May 2023 that was attended by international experts spanning atmospheric, oceanographic, and social sciences as well as legal specialists (see inside back cover). At the workshop, we reviewed prior OIF studies, distilled the lessons learned, and proposed several paths forward over the next decade to lay the foundation for evaluating OIF for marine CDR. Our discussion very quickly resulted in a recommendation for the need to establish multiple “Ocean Iron Observatories’’ where, through observations and modeling, we would be able to assess with a high degree of certainty both the durable removal of atmospheric carbon dioxide—which we term the “centennial tonne”—and the ecological response of the ocean. In a five-year phase I period, we prioritize five major research activities: 1. Next generation field studies: Studies of long-term (durable) carbon storage will need to be longer (year or more) and larger (>10,000 km2) than past experiments, organized around existing tools and models, but with greater reliance on autonomous platforms. While prior studies suggested that ocean systems return to ambient conditions once iron infusion is stopped, this needs to be verified. We suggest that these next field experiments take place in the NE Pacific to assess the processes controlling carbon removal efficiencies, as well as the intended and unintended ecological and geochemical consequences. 2. Regional, global and field study modeling Incorporation of new observations and model intercomparisons are essential to accurately represent how iron cycling processes regulate OIF effects on marine ecosystems and carbon sequestration, to support experimental planning for large-scale MRV, and to guide decision making on marine CDR choices. 3. New forms of iron and delivery mechanisms Rigorous testing and comparison of new forms of iron and their potential delivery mechanisms is needed to optimize phytoplankton growth while minimizing the financial and carbon costs of OIF. Efficiency gains are expected to generate responses closer to those of natural OIF events. 4. Monitoring, reporting, and verification: Advances in observational technologies and platforms are needed to support the development, validation, and maintenance of models required for MRV of large-scale OIF deployment. In addition to tracking carbon storage and efficiency, prioritizing eMRV will be key to developing regulated carbon markets. 5. Governance and stakeholder engagement: Attention to social dimensions, governance, and stakeholder perceptions will be essential from the start, with particular emphasis on expanding the diversity of groups engaged in marine CDR across the globe. This feedback will be a critical component underlying future decisions about whether to proceed, or not, with OIF for marine CDR. Paramount in the plan is the need to move carefully. Our goal is to conduct these five activities in parallel to inform decisions steering the establishment of ocean iron observatories at multiple locations in phase II. When completed, this decadal plan will provide a rich knowledge base to guide decisions about if, when, where, and under what conditions OIF might be responsibly implemented for marine CDR. The consensus of our workshop and this report is that now is the time for actionable studies to begin. Quite simply, we suggest that some form of marine CDR will be essential to slow down and reverse the most severe consequences of our disrupted climate. OIF has the potential to be one of these climate mitigation strategies. We have the opportunity and obligation to invest in the knowledge necessary to ensure that we can make scientifically and ethically sound decisions for the future of our planet.
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