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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 taboueThe 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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Lin, Rongbin. "UAV Intelligent Path Planning for Wilderness Search and Rescue." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1759.
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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human-behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes Chi-squared test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors. Once a probability distribution map is in place, areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a Unmanned Aerial Vehicle (UAV) to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this path-planning problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the optimal solution, making efficient use of the limited UAV flying time. The capability of planning a path with a set destination also enables the UAV operator to plan a path strategically while letting the UAV plan the path locally.
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Eng, Pillar C. S. "Path planning, guidance and control for a UAV forced landing." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/43898/1/Pillar_Eng_Thesis.pdf.
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A forced landing is an unscheduled event in flight requiring an emergency landing, and is most commonly attributed to engine failure, failure of avionics or adverse weather. Since the ability to conduct a successful forced landing is the primary indicator for safety in the aviation industry, automating this capability for unmanned aerial vehicles (UAVs) will help facilitate their integration into, and subsequent routine operations over civilian airspace. Currently, there is no commercial system available to perform this task; however, a team at the Australian Research Centre for Aerospace Automation (ARCAA) is working towards developing such an automated forced landing system. This system, codenamed Flight Guardian, will operate onboard the aircraft and use machine vision for site identification, artificial intelligence for data assessment and evaluation, and path planning, guidance and control techniques to actualize the landing. This thesis focuses on research specific to the third category, and presents the design, testing and evaluation of a Trajectory Generation and Guidance System (TGGS) that navigates the aircraft to land at a chosen site, following an engine failure. Firstly, two algorithms are developed that adapts manned aircraft forced landing techniques to suit the UAV planning problem. Algorithm 1 allows the UAV to select a route (from a library) based on a fixed glide range and the ambient wind conditions, while Algorithm 2 uses a series of adjustable waypoints to cater for changing winds. A comparison of both algorithms in over 200 simulated forced landings found that using Algorithm 2, twice as many landings were within the designated area, with an average lateral miss distance of 200 m at the aimpoint. These results present a baseline for further refinements to the planning algorithms. A significant contribution is seen in the design of the 3-D Dubins Curves planning algorithm, which extends the elementary concepts underlying 2-D Dubins paths to account for powerless flight in three dimensions. This has also resulted in the development of new methods in testing for path traversability, in losing excess altitude, and in the actual path formation to ensure aircraft stability. Simulations using this algorithm have demonstrated lateral and vertical miss distances of under 20 m at the approach point, in wind speeds of up to 9 m/s. This is greater than a tenfold improvement on Algorithm 2 and emulates the performance of manned, powered aircraft. The lateral guidance algorithm originally developed by Park, Deyst, and How (2007) is enhanced to include wind information in the guidance logic. A simple assumption is also made that reduces the complexity of the algorithm in following a circular path, yet without sacrificing performance. Finally, a specific method of supplying the correct turning direction is also used. Simulations have shown that this new algorithm, named the Enhanced Nonlinear Guidance (ENG) algorithm, performs much better in changing winds, with cross-track errors at the approach point within 2 m, compared to over 10 m using Park's algorithm. A fourth contribution is made in designing the Flight Path Following Guidance (FPFG) algorithm, which uses path angle calculations and the MacCready theory to determine the optimal speed to fly in winds. This algorithm also uses proportional integral- derivative (PID) gain schedules to finely tune the tracking accuracies, and has demonstrated in simulation vertical miss distances of under 2 m in changing winds. A fifth contribution is made in designing the Modified Proportional Navigation (MPN) algorithm, which uses principles from proportional navigation and the ENG algorithm, as well as methods specifically its own, to calculate the required pitch to fly. This algorithm is robust to wind changes, and is easily adaptable to any aircraft type. Tracking accuracies obtained with this algorithm are also comparable to those obtained using the FPFG algorithm. For all three preceding guidance algorithms, a novel method utilising the geometric and time relationship between aircraft and path is also employed to ensure that the aircraft is still able to track the desired path to completion in strong winds, while remaining stabilised. Finally, a derived contribution is made in modifying the 3-D Dubins Curves algorithm to suit helicopter flight dynamics. This modification allows a helicopter to autonomously track both stationary and moving targets in flight, and is highly advantageous for applications such as traffic surveillance, police pursuit, security or payload delivery. Each of these achievements serves to enhance the on-board autonomy and safety of a UAV, which in turn will help facilitate the integration of UAVs into civilian airspace for a wider appreciation of the good that they can provide. The automated UAV forced landing planning and guidance strategies presented in this thesis will allow the progression of this technology from the design and developmental stages, through to a prototype system that can demonstrate its effectiveness to the UAV research and operations community.
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Sabo, Chelsea. "UAV Two-Dimensional Path Planning In Real-Time Using Fuzzy Logic." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1313755639.
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Gauthier, Jason A. "Quadrotor UAV Flight Control with Integrated Mapping and Path Planning Capabilities." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1609779989188732.
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Jerker, Bergström. "Path Planning with Weighted Wall Regions using OctoMap." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-67682.
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In the work of the Control Engineering research group of the Department of Computer Science, Electrical and Space Engineering, Signals and systems at Luleå University of Technology a need had arisen for a path planning algorithm. The ongoing research with Unmanned Aerial Vehicles(UAVs) had so far been done with any complicated paths being created manually with waypoints set by the uses. To remove this labourious part of the experimental process a path should be generated automatically by simply providing a program with the position of the UAV, the goal to which the user wants it to move, as well as information about the UAV's surroundings in the form of a 3D map.In addition to simply finding an available path through a 3D environment the path should also be adapted to the risks that the physical environment poses to a flying robot. This was achieved by adapting a previously developed algorithm, which did the simple path planning task well, by adding a penalty weight to areas near obstacles, pushing the generated path away from them.The planner was developed working with the OctoMap map system which represents the physical world by segmenting it into cubes of either open or occupied space. The open segments of these maps could then be used as vertices of a graph that the planning algorithm could traverse.The algorithm itself was written in C++ as a node of the Robot Operating System(ROS) software framework to allow it to smoothly interact with previously developed software used by the Control Engineering Robotics Group.The program was tested by simulations where the path planner ROS node was sent maps as well as UAV position and intended goal. These simulations provided valid paths, with the performance of the algorithm as well as the quality of the paths being evaluated for varying configurations of the planners parameters.The planner works well in simulation and is deemed ready for use in practical experiments.
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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-search performed by an energy-constrained UAV, which is tasked to simultaneously search for a lost ground target and explore for thermals to regain energy. This problem is termed informative soaring (IFS) and combines informative path planning (IPP) with energy constraints. IFS is shown to be NP-hard by showing that it has a similar problem structure to the weight-constrained shortest path problem with replenishments. While an optimal solution may not exist in polynomial time, this thesis proposes path planning algorithms based on informed tree search to find high quality plans with low computational cost. This thesis addresses complex probabilistic belief maps and three primary contributions are presented: • First, IFS is formulated as a graph search problem by observing that any feasible long-term plan must alternate between 1) information gathering between thermals and 2) replenishing energy within thermals. This is a first step to reducing the large search state space. • The second contribution is observing that a complex belief map can be viewed as a collection of information clusters and using a divide and conquer approach, cluster tree search (CTS), to efficiently find high-quality plans in the large search state space. In CTS, near-greedy tree search is used to find locally optimal plans and two global planning versions are proposed to combine local plans into a full plan. Monte Carlo simulation studies show that CTS produces similar plans to variations of exhaustive search, but runs five to 20 times faster. The more computationally efficient version, CTSDP, uses dynamic programming (DP) to optimally combine local plans. CTSDP is executed in real time on board a UAV to demonstrate computational feasibility. • The third contribution is an extension of CTS to unknown drifting thermals. A thermal exploration map is created to detect new thermals that will eventually intercept clusters, and therefore be valuable to the mission. Time windows are computed for known thermals and an optimal cluster visit schedule is formed. A tree search algorithm called CTSDrift combines CTS and thermal exploration. Using 2400 Monte Carlo simulations, CTSDrift is evaluated against a Full Knowledge method that has full knowledge of the thermal field and a Greedy method. On average, CTSDrift outperforms Greedy in one-third of trials, and achieves similar performance to Full Knowledge when environmental conditions are favourable.
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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 path when there are unexplored parts between the vehicle and the goal, it is proposed to test paths to the goal location from every vertex in the planning graph to get a preliminary estimate of the total cost for each partial path in the planning tree. The path with the lowest cost given the available information can thus be selected, even though it partly goes through unknown space. For cases when no preliminary paths can be obtained due to obstacles, dynamic resizing of the sampling region is implemented increasing the region from which new nodes are sampled. This method using each of the three different algorithms variants, RRT, RRT*, and RRT-u, is tested for performance and the three variants are compared against each other using several test cases in a realistic simulation environment. KeywordsDetta examensarbete undersöker metoder för att utföra dynamisk ruttplanering i tre dimensioner, med applicering på obemannade luftfarkoster. Tre olika ruttplaneringsalgoritmer testas, vilka är baserade på snabbt-utforskande slumpmässiga träd (RRT): den ursprungliga RRT, RRT*, och en föreslagen variant, RRT-u, vilken skiljer sig från dom två första algoritmerna genom att ta hänsyn till dynamiska begränsningar och använda konstanta accelerationer över delar av rutten. Ruttplaneraren används också i okända miljöer. För att välja en rutt när det finns outforskade delar mellan farkosten och målet föreslås det att testa rutten till målpunkten från varje nod i som ingår i planeringsträdet för att erhålla en preliminär total kostnad till målpunkten. Rutten med lägsta kostanden kan då väljas, givet tillgänglig information, även om den delvis går genom outforskade delar. För tillfällen när inga preliminära rutter kan erhållas på grund av hinder har dynamisk storleksjustering av samplingsområdet implementerats för att öka området från vilket nya noder samplas. Den här metoden har testats med dom tre olika algoritmvarianterna, RRT, RRT*, och RRT-u, och dom tre varianterna jämförs med avseende på prestanda i ett flertal testfall i en realistisk simuleringsmiljö.
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Rathore, Aishvarya. "Quality Analysis of UAV based 3D Reconstruction and its Applications in Path Planning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658323958222.
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Baker, Chris. "A combined mechanism for UAV explorative path planning, task allocation and predictive placement." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/405212/.
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The use of Unmanned Aerial Vehicles (UAVs) is becoming ever more common by people or organisations who wish to get information about an area quickly and without a human presence. As a result, there has been a concerted effort to develop systems that allow the deployment of UAVs in disaster scenarios, in order to aid first responders with collecting imagery and other sensory data without putting human lives at risk. In particular, work has focused on developing autonomous systems to minimise the involvement of overstretched first responder personnel, and to ensure action can be taken by the UAVs quickly, co-operatively, and with close to optimal results. Key to this work, is the idea of enabling coordinated UAVs to explore a disaster space to discover incidents and then to allow more detailed examination, imagery, or sensing of these locations. Consequently, in this thesis we examine the challenge of coordinating exploratory and task-responsive UAVs in the presence of prior (but uncertain) beliefs about incident locations, and the combination of their roles together. To do this, we first identify the key components of such a system as: path planning, task allocation, and using belief data for predictive UAV placement. Subsequently, we introduce our contributions in the form of a complete, decentralised system for a single explorative path planner to minimise the time to identify incidents, to allocate incidents to UAVs as tasks, and to place UAVs prior to new tasks being found. Having demonstrated the efficacy of this solution in experimental scenarios, we extend the formulation of our explorative path-planning problem to multiple UAVs by constructing a coordinated, factored Monte-Carlo Tree Search algorithm for use in a discretised space representation of a disaster area. Subsequently, we detail the performance of our new algorithm against uncoordinated alternatives using real data from the 2010 Haiti earthquake. We demonstrate the performance benefits of our method via the metric of people discovered in the simulation; showing improvements of up to 23% in cases with ten UAVs. This is the first application of this technique to very large action spaces of the type encountered in realistic disaster scenarios. Finally, we modify our coordinated exploration algorithm to function in a continuous action space. This represents the first example of a continuous factored coordinated Monte-Carlo Tree Search algorithm. We evaluated this algorithm on the same Haiti dataset as the discretised version, but with a new sensor model simulating mobile phone signal detection to represent the types of sensors deployed by first responders. In addition to the benefits of a more realistic model of the environment, we found improvements in survivor localisation times of up to 20% over the discrete algorithm; demonstrating the value in our approach. As such, the contributions presented in this thesis advance the state of the art in UAV coordination algorithms, and represent a progression towards the widespread deployment of autonomous platforms that can aid rescue workers in disaster situations and—ultimately—save lives.
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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 greater flexibility and processing power, while maintaining backwards-compatibility and limiting infringement on the real-time processing of the FCS itself. The system was fully simulated via both hardware and software simulators, and ultimately the system was field tested using multiple vehicles collaboratively searching a defined area.
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Öst, Gustav. "Search path generation with UAV applications using approximate convex decomposition." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77353.
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This work focuses on the problem that pertains to area searching with UAVs. Specifically developing algorithms that generate flight paths that are short with- out sacrificing flyability. For instance, very sharp turns will compromise flyability since fixed wing aircraft cannot make very sharp turns. This thesis provides an analysis of different types of search methods, area decompositions, and combi- nations thereof. The search methods used are side to side searching and spiral searching. In side to side searching the aircraft goes back and forth only making 90-degree turns. Spiral search searches the shape in a spiral pattern starting on the outer perimeter working its way in. The idea being that it should generate flight paths that are easy to fly since all turns should be with a large turn radii. Area decomposition is done to divide complex shapes into smaller more manage- able shapes. The report concludes that with the implemented methods the side to side scanning method without area decomposition yields good and above all very reliable results. The reliability stems from the fact that all turns are 90 degrees and that algorithm never get stuck or makes bad mistakes. Only having 90 degree turns results in only four different types of turns. This allows the airplanes behav- ior along the route to be predictable after flying the first four turns. Although this assumes that the strength of the wind is a greater influence than the turbulences effect on the aircraft’s flight characteristics. This is a very valuable feature for an operator in charge of a flight. The other tested methods and area decompositions often yield a shorter flight path, however, despite extensive adjustments to the algorithms they never came to handle all cases in a satisfactory manner. These methods may also generate any kind of turn at any time, including turns of nearly 180 degrees. These turns can lead to an airplane missing the intended flight path and thus missing to scan the intended area properly. Area decomposition proves to be really effective only when the area has many protrusions that stick out in different directions, think of a starfish shape. In these cases the side to side algo- rithm generate a path that has long legs over parts that are not in the search area. When the area is decomposed the algorithm starts with, for example, one arm of the starfish at a time and then search the rest of the arms and body in turn.
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Radford, Scott Carson. "Real-Time Roadway Mapping and Ground Robotic Path Planning Via Unmanned Aircraft." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50431.
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The thesis details the development of computer vision and path planning algorithms in order to map an area via UAV aerial imagery and aid a UGV in navigating a roadway when the road conditions are not previously known (i.e. disaster situations). Feature detection was used for transform calculation and image warping to create mosaics. A continuous extension using dynamic cropping based on newly gathered images was used to improve performance and computation time. Road detection using k-means segmentation and binary image morphing was applied to aerial imagery with image shifting tracked by the mosaicking to develop a large road map. Improvements to computation time were developed using k-means for calibration at intervals and nearest neighbor calculating for each image. This showed a greatly reduced computation time for a series of images with only 1-2% error compared to regular k-means segmentation. Path planning for the UAV utilized a traveling wave applied to the traveling salesman genetic algorithm solution to prioritize close targets and facilitate UGV deployment. Based on the large map of road locations and road detection method, the Rapidly-exploring Random Tree (RRT) algorithm was modified for real-time application and efficient data processing. Considerations of incomplete maps and goal adjustments was also incorporated. Finally, aerial imagery from an actual UAV flight was processed using these algorithms to validate and test flight parameters. Testing of different flight parameters showed the desired image overlay of 50% to give accurate mosaics. It also helped to develop a benchmark for the altitude, image resolution and frequency for flights. Vehicle requirements and algorithm limitations for future applications of this system are also discussed.Master of Science
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Han, Kyung Min. "Collision free path planning algorithms for robot navigation problem." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5021.
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Thesis (M.S.)--University of Missouri-Columbia, 2007.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 29, 2008) Includes bibliographical references.
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Pitre, Ryan R. "An Information Value Approach to Route Planning for UAV Search and Track Missions." ScholarWorks@UNO, 2011. http://scholarworks.uno.edu/td/1403.
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This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them.
UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties.
Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target.
The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G.
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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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Sinha, Koel. "Path Planning for a UAV in an Agricultural Environment to Tour and Cover Multiple Neighborhoods." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/79731.
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This work focuses on path planning for an autonomous UAV to tour and cover multiple
regions in the shortest time. The three challenges to be solved are - finding the right
optimal order to tour the neighborhoods, determining entry and exit points to
neighborhoods, and covering each neighborhood. Two approaches have been explored and compared to achieve this goal - a TSP - Greedy and TSP - Dijkstra's. Both of them use a TSP solution to determine the optimal order of touring. They also use the same back and forth motion to cover each region. However, while the first approach uses a brute force to determine the the next closest node of entry or exit, the second approach utilizes the Dijkstra's algorithm to compute all possible paths to every node in the graph, and therefore choose the shortest pairs of entry and exit for each region, that would generate the shorter path, overall. The main contribution of this work is to implement an existing algorithm to combine the touring and covering problem, and propose a new algorithm to perform the same. Empirical results comparing performances of both approaches are included. Hardware experiments are performed on a spraying hexacopter, using the TSP - Greedy approach. Unique system characteristics are studied to make conclusions about stability of the platform. Future directions are identified to improve both software and hardware performance.Master of Science
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Jung, Dongwon Jung. "Hierarchical Path Planning and Control of a Small Fixed-wing UAV: Theory and Experimental Validation." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19781.
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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2008.Committee Chair: Tsiotras, Panagiotis; Committee Member: Corban, Eric; Committee Member: Feron, Eric; Committee Member: Johnson, Eric; Committee Member: Vachtsevanos, George.
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Morin, Michael. "Multi-Criteria Path Planning with Terrain Visibility Constraints: The Optimal Searcher Path Problem with Visibility." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27495/27495.pdf.
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Kindl, Mark Richard. "A stochastic approach to path planning in the Weighted-Region Problem." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26789.
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Walker, David H. "Coordinated UAV Target Assignment Using Distributed Calculation of Target-Task Tours." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/130.
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This thesis addresses the improvement of cooperative task allocation to vehicles in multiple-vehicle, multiple-target scenarios through the use of more effective preplanned tours. Effective allocation of vehicles to targets requires knowledge of both the team objectives and the contributions that individual vehicles can make toward accomplishing team goals. This is primarily an issue of individual vehicle path planning --- determining the path the vehicles will follow to accomplish individual and team goals. Conventional methods plan optimal point-to-point path segments that often result in lengthy and suboptimal tours because the trajectory neither considers future tasks nor the overall path. However, cooperation between agents is improved when the team selects vehicle assignments based on the ability to complete immediate and subsequent tasks. This research demonstrates that planning more efficient tour paths through multiple targets results in better use of individual vehicle resources, faster completion of team objectives, and improved overall cooperation between agents. This research presents a method of assigning unmanned aerial vehicles to targets to improve cooperation. A tour path planning method was developed to overcome shortcomings of traditional point-to-point path planners, and is extended to the specific tour-planning needs of this problem. The planner utilizes an on-line learning heuristic search to find paths that accomplish team goals in the shortest flight time. The learning search planner uses the entire sensor footprint, more efficiently plans tours through closely packed targets, and learns the best order for completion of the multiple tasks. The improved planner results in assignment completion times that range on average between 1.67 and 2.41 times faster, depending on target spread. Assignments created from preplanned tours make better use of vehicle resources and improve team cooperation. Path planning and assignment selection are accomplished in near real-time through the use of path heuristics and assignment cost estimates to reduce the problem size to tractable levels. Assignments are ordered according to estimated or predicted value. A reduced number of ordered assignments is considered and evaluated to control problem growth. The estimates adequately represent the actual assignment value, effectively reduce problem size, and produce near-optimal paths and assignments for near-real-time applications.
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Bishop, Jacob L. "Search Pattern Generation and Path Management for Search over Rough Terrain with a Small UAV." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2275.
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Search operations can be described by the interaction between three entities: the target, the sensor, and the environment. Past treatments of the search problem have focused primarily on the interaction between the sensor and the target. The effects that the environment has on the target and sensor have been greatly simplified or ignored completely. The wilderness search and rescue scenario is one case in which these interactions cannot be safely ignored. Using the wilderness search and rescue problem as our motivating example, we develop an algorithm for planning search paths for a small unmanned aerial vehicle (UAV) over rough terrain environments that provide complete coverage of the specified terrain region while minimizing effort wasted on duplicate coverage. The major components of this algorithm include 1) breaking the search region into smaller sub-regions that are easier to deal with, and 2) planning the search for each of these sub-regions. The original contributions of this thesis focus on the latter of these two components. We use a method based on the directional offset of terrain contours to produce paths on the terrain for the sensor to observe as the UAV follows the flight path. We then employ directional-offset methods again by moving in the direction along the terrain normal from the sensor path to generate a flight path that lies in the air a specified distance away from the points on the terrain that are to be observed. These two paths are linked in a way that provides the sensor with an ample viewing opportunity of the terrain regions below. We implement this planning algorithm in software with Matlab, and provide a complete simulation of a UAV that follows the planned search pattern. Our planning algorithm produced search paths that were 94 to 100 percent complete in test scenarios for several rough-terrain regions. Missed regions for these plans were near the search boundaries, and coverage could easily be provided by subsequent plans. We recommend the study of region segmentation, with careful consideration of planning algorithms as the major focus of future work.
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Gilson, Maximillian Andrew. "Fault-tolerant mapping and localization for Quadrotor UAV." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright157865858408435.
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Krawiec, Bryan Michael. "A*-Based Path Planning for an Unmanned Aerial and Ground Vehicle Team in a Radio Repeating Operation." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/32545.
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In the event of a disaster, first responders must rapidly gain situational awareness about the environment in order to plan effective response operations. Unmanned ground vehicles are well suited for this task but often require a strong communication link to a remote ground station to effectively relay information. When considering an obstacle-rich environment, non-line-of-sight conditions and naive navigation strategies can cause substantial degradations in radio link quality. Therefore, this thesis incorporates an unmanned aerial vehicle as a radio repeating node and presents a path planning strategy to cooperatively navigate the vehicle team so that radio link health is maintained.
This navigation technique is formulated as an A*-based search and this thesis presents the formulation of this path planner as well as an investigation into strategies that provide computational efficiency to the search process. The path planner uses predictions of radio signal health at different vehicle configurations to effectively navigate the vehicles and simulations have shown that the path planner produces favorable results in comparison to several conceivable naive radio repeating variants. The results also show that the radio repeating path planner has outperformed the naive variants in both simulated environments and in field testing where a Yamaha RMAX unmanned helicopter and a ground vehicle were used as the vehicle team.
Since A* is a general search process, this thesis also presents a roadway detection algorithm using A* and edge detection image processing techniques. This algorithm can supplement unmanned vehicle operations and has shown favorable performance for images with well-defined roadways.Master of Science
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Millar, Brett Wayne. "Multi-Resolution Obstacle Mapping with Rapidly-Exploring Random Tree Path Planning for Unmanned Air Vehicles." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2620.
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Unmanned air vehicles (UAVs) have become an important area of research. UAVs are used in many environments which may have previously unknown obstacles or sources of danger. This research addresses the problem of obstacle mapping and path planning while the UAV is in flight. Online obstacle mapping is achieved through the use of a multi-resolution map. As sensor information is received, a quadtree is built up to hold the information based upon the uncertainty associated with the measurement. Once a quadtree map of obstacles is built up, we desire online path re-planning to occur as quickly as possible. We introduce the idea of a quadtree rapidly-exploring random tree (RRT), which will be used as the online path re-planning algorithm. This approach implements a variable sized step instead of the fixed-step size usually used in the RRT algorithm. This variable step uses the structure of the quadtree to determine the step size. The step size grows larger or smaller based upon the size of the area represented by the quadtree it passes through. Finally this approach is tested in a simulation environment. The results show that the quadtree RRT requires fewer steps on average than a standard RRT to find a path through an area. It also has a smaller variance in the number of steps taken by the path planning algorithm in comparison to the standard RRT.
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Christie, Gordon A. "Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/83807.
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Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g. road maps). This is especially true in disaster response scenarios, where existing maps are now out of date. Areas without GPS are another concern, especially when the involved systems are tasked with navigating a path planned by a remote base station. Scene understanding via robots' perception data (e.g. images) can greatly assist in overcoming these challenges. This dissertation makes three contributions that help overcome these challenges, where there is a focus on the application of autonomously searching for radiation sources with unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) in unknown and unstructured environments. The three main contributions of this dissertation are: (1) An approach to overcome the challenges associated with simultaneously trying to understand 2D and 3D information about the environment. (2) Algorithms and experiments involving scene understanding for real-world autonomous search tasks. The experiments involve a UAV and a UGV searching for potentially hazardous sources of radiation is an unknown environment. (3) An approach to the registration of a UGV in areas without GPS using 2D image data and 3D data, where localization is performed in an overhead map generated from imagery captured in the air.Ph. D.
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Kamrani, Farzad. "Simulation-based Optimization and Decision Making with Imperfect Information." Doctoral thesis, KTH, Programvaru- och datorsystem, SCS, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-50171.
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The purpose of this work is to provide simulation-based support for making optimal (or near-optimal) decisions in situations where decision makers are faced with imperfect information. We develop several novel techniques and algorithms for simulation-based optimization and decision support and apply them to two categories of problems: (i) Unmanned Aerial Vehicle (UAV) path planning in search operations, and; (ii) optimization of business process models. Common features of these two problems for which analytical approaches are not available, are the presence of imperfect information and their inherent complexity. In the UAV path planning problem, the objective is to define the path of a UAV searching for a target on a known road network. It is assumed that the target is moving toward a goal and we have some uncertain information about the start point of the target, its velocity, and the final goal of the target. The target does not take evasive action to avoid being detected. The UAV is equipped with a sensor, which may detect the target once it is in the sensor’s scope. Nevertheless, the detection process is uncertain and the sensor is subject to both false-positive and false-negative errors. We propose three different solutions, two of which are simulation-based. The most promising solution is an on-line simulation-based method that estimates the location of the target by using a Sequential Monte Carlo (SMC) method. During the entire mission, different UAV paths are simulated and the one is chosen that most reduces the uncertainty about the location of the target. In the optimization of the business process models, several different but related problems are addressed: (i) we define a measure of performance for a business process model based on the value added by agents (employees) to the process; (ii) we use this model for optimization of the business process models. Different types of processes are distinguished and methods for finding the optimal or near-optimal solutions are provided; (iii) we propose a model for estimating the performance of collaborative agents. This model is used to solve a class of Assignment Problems (AP), where tasks are assigned to collaborative agents; (iv) we propose a model for team activity and the performance of a team of agents. We introduce different collaboration strategies between agents and a negotiation algorithm for resolving conflicts between agents. We compare the effect of different strategies on the output of the team. Most of the studied cases are complex problems for which no analytical solution is available. Simulation methods are successfully applied to these problems. They are shown to be more general than analytical models for handling uncertainty since they usually have fewer assumptions and impose no restrictions on the probability distributions involved. Our investigation confirms that simulation is a powerful tool for providing decision-making support. Moreover, our proposed algorithms and methods in the accompanying articles contribute to providing support for making optimal and in some cases near-optimal decisions: (i) our tests of the UAV simulation-based search methods on a simulator show that the on-line simulation method has generally a high performance and detects the target in a reasonable time. The performance of this method was compared with the detection time when the UAV had the exact information about the initial location of the target, its velocity, and its path (minimum detection time). This comparison indicated that the online simulation method in many cases achieved a near-optimal performance in the studied scenario; (ii) our business process optimization framework combines simulation with the Hungarian method and finds the optimal solution for all cases where the assignment of tasks does not change the workflow of the process. For the most general cases, where the assignment of tasks may change the workflow, we propose an algorithm that finds near-optimal solutions. In this algorithm, simulation, which deals with the uncertainty in the process, is combined with the Hungarian method and hill-climbing heuristics. In the study of assigning tasks to collaborative agents we suggest a Genetic Algorithm (GA) that finds near-optimal solutions with a high degree of accuracy, stability, scalability and robustness. While investigating the effect of different agent strategies on the output of a team, we find that the output of a team is near-optimal, when agents choose a collaboration strategy that follows the principle of least effort (Zipf’s law) and use our suggested algorithm for negotiation and resolving conflicts.QC 20111202
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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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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, CaliforniaBrigham 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. This paper outlines our approach and successes in facilitating this accomplishment at the undergraduate level.
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Wzorek, Mariusz. "Selected Aspects of Navigation and Path Planning in Unmanned Aircraft Systems." Licentiate thesis, Linköpings universitet, UASTECH – Teknologier för autonoma obemannade flygande farkoster, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71147.
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Unmanned aircraft systems (UASs) are an important future technology with early generations already being used in many areas of application encompassing both military and civilian domains. This thesis proposes a number of integration techniques for combining control-based navigation with more abstract path planning functionality for UASs. These techniques are empirically tested and validated using an RMAX helicopter platform used in the UASTechLab at Linköping University. Although the thesis focuses on helicopter platforms, the techniques are generic in nature and can be used in other robotic systems. At the control level a navigation task is executed by a set of control modes. A framework based on the abstraction of hierarchical concurrent state machines for the design and development of hybrid control systems is presented. The framework is used to specify reactive behaviors and for sequentialisation of control modes. Selected examples of control systems deployed on UASs are presented. Collision-free paths executed at the control level are generated by path planning algorithms.We propose a path replanning framework extending the existing path planners to allow dynamic repair of flight paths when new obstacles or no-fly zones obstructing the current flight path are detected. Additionally, a novel approach to selecting the best path repair strategy based on machine learning technique is presented. A prerequisite for a safe navigation in a real-world environment is an accurate geometrical model. As a step towards building accurate 3D models onboard UASs initial work on the integration of a laser range finder with a helicopter platform is also presented. Combination of the techniques presented provides another step towards building comprehensive and robust navigation systems for future UASs.
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Skoglar, Per. "Modelling and control of IR/EO-gimbal for UAV surveillance applications." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1281.
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This thesis is a part of the SIREOS project at Swedish Defence Research Agency which aims at developing a sensor system consisting of infrared and video sensors and an integrated navigation system. The sensor system is placed in a camera gimbal and will be used on moving platforms, e.g. UAVs, for surveillance and reconnaissance. The gimbal is a device that makes it possible for the sensors to point in a desired direction.
In this thesis the sensor pointing problem is studied. The problem is analyzed and a system design is proposed. The major blocks in the system design are gimbal trajectory planning and gimbal motion control. In order to develop these blocks, kinematic and dynamic models are derived using techniques from robotics. The trajectory planner is based on the kinematic model and can handle problems with mechanical constraints, kinematic singularity, sensor placement offset and reference signal transformation.
The gimbal motion controller is tested with two different control strategies, PID and LQ. The challenge is to perform control that responds quickly, but do not excite the damping flexibility too much. The LQ-controller uses a linearization of the dynamic model to fulfil these requirements.
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Prusakiewicz, Lukas, and Simon Tönnes. "Comparison of autonomous waypoint navigation methods for an indoor blimp robot." Thesis, KTH, Mekatronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284458.
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The Unmanned Aerial Vehicle (UAV) has over the last years become an increasingly prevalent technology in several sectors of modern society. Many UAVs are today used in a wide series of applications, from disaster relief to surveillance. A recent initiative by the Swedish Sea Rescue Society (SSRS) aims to implement UAVs in their emergency response. By quickly deploying drones to an area of interest, an assessment can be made, prior to personnel getting there, thus saving time and increasing the likelihood of a successful rescue operation. An aircraft like this, that will travel great distances, have to rely on a navigation system that does not require an operator to continuously see the vehicle. To travel to its goal, or search an area, the operator should be able to define a travel route that the UAV follows, by feeding it a series of waypoints. As an initial step towards that kind of system, this thesis has developed and tested the concept of waypoint navigation on a small and slow airship/blimp, in a simulated indoor environment. Mainly, two commonly used navigation algorithms were tested and compared. One is inspired by a sub-category of machine learning: reinforcement learning (RL), and the other one is based on the rapidly exploring random tree (RRT) algorithm. Four experiments were conducted to compare the two methods in terms of travel distance, average speed, energy efficiency, as well as robustness towards changes in the waypoint configurations. Results show that when the blimp was controlled by the best performing RL-based version, it generally travelled a more optimal (distance-wise) path than the RRT-based method. It also, in most cases, proved to be more robust against changes in the test tracks, and performed more consistently over different waypoint configurations. However, the RRT approach usually resulted in a higher average speed and energy efficiency. Also, the RL algorithm had some trouble navigating tracks where a physical obstacle was present. To sum up, the choice of algorithm depends on which parameters are prioritized by the blimp operator for a certain track. If a high velocity and energy efficiency is desirable, the RRT-based method is recommended. However, if it is important that the blimp travels as short a distance as possible between waypoints, and a higher degree of consistency in its performance is wanted, then the RL-method should be used. Moving forward from this report, toward the future implementation of both methods in rescue operations, it would be reasonable to analyze their performance under more realistic conditions. This can be done using a real indoor airship. Looking at how hardware that do not exceed the payload of the blimp can execute both methods and how the blimp will determine its position and orientation is recommended. It would also be interesting to see how different reward function affect the performance of the blimp.Den obemannade luftfarkosten (UAV) har under de senaste åren blivit en teknik vars användning blivit allt vanligare i flera sektorer av det moderna samhället. Olika sorters UAV robotar associeras idag med en omfattande serie användningsområden, från katastrofhjälp till övervakning. Ett nyligen påbörjat initiativ från svenska sjöräddningssällskapet (SSRS) syftar till att implementera drönare i deras utryckningar. Genom att snabbt sända drönare till platsen i fråga, kan en bedömning göras innan personal kommer dit, vilket sparar tid och ökar sannolikheten för en framgångsrik räddningsaktion. En farkost som denna, som kommer att resa långa sträckor, måste förlita sig på ett navigationssystem som inte kräver att en operatör kontinuerligt ser farkosten. För att resa till sitt mål, eller söka av ett område, bör operatören kunna definiera en resväg som drönaren följer genom att ge den en serie vägpunkter. Som ett inledande steg mot den typen av system har denna uppsats utvecklat och testat begreppet vägpunktsnavigering på ett litet och långsamt luftskepp/blimp, i en simulerad inomhusmiljö. Huvudsakligen testades och jämfördes två vanligt förekommande navigationsalgoritmer. En inspirerad av en underkategori till maskininlärning: förstärkningsinlärning (RL), och den andra baserad på rapidly exploring random tree (RRT) algoritmen. Fyra experiment utfördes för jämföra båda metoderna med avseende på färdsträcka, medelhastighet, energieffektivitet samt robusthet gentemot ändringar i färdpunktskonfigurationerna. Resultaten visar att när blimpen kontrollerades av den bästa RL-baserade versionen åkte den generellt en mer avståndsmässigt optimal väg än när den RRT-baserade metoden användes. I de flesta fallen visade sig även RL-metoden vara mer robust mot förändringar i testbanorna, och presterade mer konsekvent över olika vägpunktskonfigurationer. RRT-metoden resulterade dock vanligtvis i en högre medelhastighet och energieffektivitet. RL-algoritmen hade också problem med att navigera banor där den behövde ta sig runt ett hinder. Sammanfattningsvis beror valet av algoritm på vilka parametrar som prioriteras av blimpoperatören för en viss bana. Om en hög hastighet och energieffektivitet är önskvärd rekommenderas den RRT-baserade metoden. Men om det är viktigt att blimpen reser så kort avstånd som möjligt mellan färdpunkterna, och har en jämnare prestanda, bör RL-metoden användas. För att ta nästa steg, mot en framtida implementering av båda metoder i räddningsoperationer, vore det rimligt att analysera deras prestanda under mer realistiska förhållanden. Detta skulle kunna göras inomhus med ett riktigt luftskepp. Författarna rekommenderar att undersöka om hårdvara som inte överstiger blimpens maxlast kan utföra båda metodernas beräkningar och hur blimpen bestämmer sin position och orientering. Det skulle också vara intressant att se hur olika belöningsfunktioner påverkar blimpens prestanda.
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Curtis, Andrew B. "Path Planning for Unmanned Air and Ground Vehicles in Urban Environments." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2270.pdf.
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Al, Sabban Wesam H. "Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82297/1/Wesam%20H_Al%20Sabban_Thesis.pdf.
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One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.
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Palmer, Jacob N. "Radio propagation analysis for improved UAV data muling of surfaced underwater sensor nodes." Scholarly Commons, 2015. https://scholarlycommons.pacific.edu/uop_etds/218.
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The present work examines waypoint selection and evaluation mechanisms for data muling water sensor nodes via unmanned air vehicle. We present a mathematical model for predicting signal strength with respect to distance and height using a two-ray propagation model in conjunction with the individual radiation patterns of transmitting and receiving antennas. Signal quality over space is then be used to select best waypoints. Packet reception rate is related to the received signal strength indicator through experimentation and serves as a data efficiency indicator. Both models are then used to gather performance metrics of several simple path planning schemes. Both hover-only and in-flight communication are compared. Packet reception rate limitations were found to dramatically limit the effectiveness of waypoint selection regardless of power efficiency.
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Young, Stephen Alexander. "Multi-level Control Architecture and Energy Efficient Docking for Cooperative Unmanned Air Vehicles." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31192.
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In recent years, significant progress has been made in improving the performance of unmanned air vehicles in terms of aerodynamic performance, endurance, autonomy, and the capability of on-board sensor packages. UAVs are now a vital part of both military actions and scientific research efforts. One of the newest classes of UAV is the high altitude long endurance or HALE UAV. This thesis considers the high-level control problem for a unique HALE mission involving cooperative solar
powered UAVs. Specifically addressed is energy efficient path planning for vehicles that physically link together in flight to form a larger, more energy efficient HALE vehicle.
Energy efficient docking is developed for the case of multiple vehicles at high altitude with negligible wind. The analysis considers a vehicle governed by a kinematic motion model with bounded turn rate in planar constant altitude flight. Docking is demonstrated using a platform-in-the-loop simulator which was developed to allow virtual networked vehicles to perform decentralized path planning and estimation of all vehicle states. Vehicle behavior is governed by a status which is commanded by a master computer and communication between vehicles is intermittent depending on each vehicleâ s assessment of situational awareness. Docking results in a larger vehicle that consumes energy at 21% of the rate of an individual vehicle and increases vehicle range by a factor of three without considering solar recharging.Master of Science
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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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Lindqvist, Björn. "Combined Control and Path Planning for a Micro Aerial Vehicle based on Non-linear MPC with Parametric Geometric Constraints." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76212.
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Using robots to navigate through un-mapped environments, specially man-made infrastructures, for the purpose of exploration or inspection is a topic that has gathered a lot of interest in the last years. Micro Aerial Vehicles (MAV's) have the mobility and agility to move quickly and access hard-to-reach areas where ground robots would fail, but using MAV's for that purpose comes with its own set of problems since any collision with the environment results in a crash. The control architecture used in a MAV for such a task needs to perform obstacle avoidance and on-line path-planning in an unknown environment with low computation times as to not lose stability. In this thesis a Non-linear Model Predictive Controller (NMPC) for obstacle avoidance and path-planning on an aerial platform will be established. Included are methods for constraining the available state-space, simulations of various obstacle avoidance scenarios for single and multiple MAVs and experimental validation of the proposed control architecture. The validity of the proposed approach is demonstrated through multiple experimental and simulation results. In these approaches, the positioning information of the obstacles and the MAV are provided by a motion-capture system. The thesis will conclude with the demonstration of an experimental validation of a centralized NMPC for collision avoidance of two MAV's.
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SPANO', CUOMO LUCA. "Planning and Control Strategies for Collaborative Aerial Autonomous Vehicles." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2932745.
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Vega-Nevarez, Juan. "Online Path Planning and Control Solution for a Coordinated Attack of Multiple Unmanned Aerial Vehicles in a Dynamic Environment." Master's thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5551.
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The role of the unmanned aerial vehicle (UAV) has significantly expanded in the military sector during the last decades mainly due to their cost effectiveness and their ability to eliminate the human life risk. Current UAV technology supports a variety of missions and extensive research and development is being performed to further expand its capabilities. One particular field of interest is the area of the low cost expendable UAV since its small price tag makes it an attractive solution for target suppression. A swarm of these low cost UAVs can be utilized as guided munitions or kamikaze UAVs to attack multiple targets simultaneously. The focus of this thesis is the development of a cooperative online path planning algorithm that coordinates the trajectories of these UAVs to achieve a simultaneous arrival to their dynamic targets. A nonlinear autopilot design based on the dynamic inversion technique is also presented which stabilizes the dynamics of the UAV in its entire operating envelope. A nonlinear high fidelity six degrees of freedom model of a fixed wing aircraft was developed as well that acted as the main test platform to verify the performance of the presented algorithmsID: 031001316; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: Houman A. Sadri.; Title from PDF title page (viewed March 26, 2013).; Thesis (M.A.)--University of Central Florida, 2012.; Includes bibliographical references (p. 89-99).
M.S.E.E.
Masters
Electrical Engineering and Computing
Engineering and Computer Science
Electrical Engineering; Controls and Robotics
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Cardell, Magnus. "UAV Navigation using Local Computational Resources : Keeping a target in sight." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291229.
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When tracking a moving target, an Unmanned Aerial Vehicle (UAV) mustkeep the target within its sensory range while simultaneously remaining awareof its surroundings. However, small flight computers must have sufficientenvironmental knowledge and computational capabilities to provide real-timecontrol to function without a ground station connection. Using a Raspberry Pi4 model B, this thesis presents a practical implementation for evaluating pathplanning generators in the context of following a moving target. The practicalmodel integrates two waypoint generators for the path planning scenario: A*and 3D Vector Field Histogram* (3DVFH*). The performances of the pathplanning algorithms are evaluated in terms of the required processing time,distance from the target, and memory consumption. The simulations are runin two types of environments. One is modelled by hand with a target walkinga scripted path. The other is procedurally generated with a random walker.The study shows that 3DVFH* produces paths that follow the moving targetmore closely when the actor follows the scripted path. With a random walker,A* consistently achieves the shortest distance. Furthermore, the practicalimplementation shows that the A* algorithm’s persistent approach to detectand track objects has a prohibitive memory requirement that the Raspberry Pi4 with a 2GBRAMcannot handle. Looking at the impact of object density, the3DVFH* implementation shows no impact on distance to the moving target,but exhibits lower execution speeds at an altitude with fewer obstacles to detect.The A* implementation has a marked impact on execution speeds in the formof longer distances to the target at altitudes with dense obstacle detection.This research project also realized a communication link between thepath planning implementations and a Geographical Information System (GIS)application supported by the Carmenta Engine SDK to explore how locallystored geospatial information impact path planning scenarios. Using VMapgeospatial data, two levels of increasing geographical resolution werecompared to show no performance impact on the planner processes, but asignificant addition in memory consumption. Using geospatial informationabout a region of interest, the waypoint generation implementations are ableto query the map application about the legality of its current position.När en obemannade luftfarkost, även kallad drönare, spårar ett rörligt mål, måste drönaren behålla målet inom sensorisk räckvidd medan den håller sig uppdaterad om sin omgivning. Små flygdatorer måste dock ha tillräckligt med information om sin omgivning och nog med beräkningsresurser för att erbjuda realtidskontroll utan kommunikation med en markstation. Genom att använda en Raspberry Pi 4 modell B presenterar denna studie en praktisk applicering utav vägplanerare som utvärderas utifrån deras lämplighet att följa ett rörligt mål. Den praktiska implementationen jämför två vägplaneringsalgoritmer: A* och 3D Vector Field Histogram* (3DVFH*). Vägplaneringsalgoritmernas prestanda utvärderas genom att studera deras hastighet, avstånd från målet och minnesresurser. Vägplaneringsalgoritmerna utvärderas i två situationer. Den första är en simulationsvärld som är gjord för hand där målet rör sig efter en fördefinierad väg. Den andra är en procedurellt genererad värld där målet rör sig slumpmässigt. Studien visar att 3DVFH* producerar vägar som håller drönaren närmare målet när målet rör sig efter en fördefinierad väg. Med en slumpvandring i en procedurell värld är A* närmast målet. Resultaten från Raspberry Pi visar också att A* algoritmen sätter prohibitivt höga minneskrav på Raspberry Pi 4 som bara har 2GBRAM. Studerar man påverkan av synbara objekt på avståndet till målet så ser man ingen för 3DVFH* algoritmens egenskap att hålla sig nära, men man ser snabbare bearbetningshastighet när det är färre objekt att upptäcka. A* algoritmen ser en påverkan på dess distans från målet när fler objekt finns att upptäcka. Denna studie visar också hur en kommunikationslänk mellan vägplaneringsalgoritmer och kartapplikationer som stöds utav Carmenta Engine SDK skall implementeras. Detta används för att studera hur lokal geografisk information kan användas i ett spårningssammanhang. Genom att använda två nivåer av geografisk upplösning från VMap data, jämförs påverkan på vägplaneringarnas prestanda. Studien visar att ingen påverkan på prestandan kan ses men att kartapplikationen kräver mer minnesresurser. Genom att använda geografisk information om en region av intresse visar denna applikation hur vägplaneringsalgoritmerna kan fråga kartapplikationen om legaliteten om sin nuvarande position.