Literatura científica selecionada sobre o tema "Fleet of UAVs"
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Artigos de revistas sobre o assunto "Fleet of UAVs"
Фесенко, Герман Вікторович, e Вячеслав Сергійович Харченко. "МОДЕЛІ НАДІЙНОСТІ УГРУПОВАНЬ ФЛОТІВ БПЛА З КОВЗНИМ РЕЗЕРВУВАННЯМ ДЛЯ МОНІТОРИНГУ ПОТЕНЦІЙНО НЕБЕЗПЕЧНИХ ОБ’ЄКТІВ". RADIOELECTRONIC AND COMPUTER SYSTEMS, n.º 2 (21 de junho de 2019): 147–56. http://dx.doi.org/10.32620/reks.2019.2.14.
Texto completo da fonteThibbotuwawa, Bocewicz, Zbigniew e Nielsen. "A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions". Applied Sciences 9, n.º 19 (22 de setembro de 2019): 3972. http://dx.doi.org/10.3390/app9193972.
Texto completo da fonteGodio, Simone, Stefano Primatesta, Giorgio Guglieri e Fabio Dovis. "A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs". Information 12, n.º 2 (25 de janeiro de 2021): 51. http://dx.doi.org/10.3390/info12020051.
Texto completo da fonteLarson, Jonathan, Paul Isihara, Gabriel Flores, Edwin Townsend, Danilo R. Diedrichs, Christy Baars, Steven Kwon et al. "A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet". SIMULATION 96, n.º 8 (7 de junho de 2020): 641–53. http://dx.doi.org/10.1177/0037549720921447.
Texto completo da fonteThibbotuwawa, Amila, Grzegorz Bocewicz, Grzegorz Radzki, Peter Nielsen e Zbigniew Banaszak. "UAV Mission Planning Resistant to Weather Uncertainty". Sensors 20, n.º 2 (16 de janeiro de 2020): 515. http://dx.doi.org/10.3390/s20020515.
Texto completo da fonteGadiraju, Divija Swetha, Prasenjit Karmakar, Vijay K. Shah e Vaneet Aggarwal. "GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments". Information 15, n.º 8 (11 de agosto de 2024): 477. http://dx.doi.org/10.3390/info15080477.
Texto completo da fonteKats, Vladimir, e Eugene Levner. "Maximizing the Average Environmental Benefit of a Fleet of Drones under a Periodic Schedule of Tasks". Algorithms 17, n.º 7 (28 de junho de 2024): 283. http://dx.doi.org/10.3390/a17070283.
Texto completo da fonteBit-Monnot, Arthur, Rafael Bailon-Ruiz e Simon Lacroix. "A Local Search Approach to Observation Planning with Multiple UAVs". Proceedings of the International Conference on Automated Planning and Scheduling 28 (15 de junho de 2018): 437–45. http://dx.doi.org/10.1609/icaps.v28i1.13924.
Texto completo da fonteJosé-Torra, Ferran, Antonio Pascual-Iserte e Josep Vidal. "A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations". Sensors 18, n.º 10 (11 de outubro de 2018): 3411. http://dx.doi.org/10.3390/s18103411.
Texto completo da fonteHe, Ping Chuan, e Shu Ling Dai. "Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning". Advanced Materials Research 846-847 (novembro de 2013): 1189–96. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1189.
Texto completo da fonteTeses / dissertações sobre o assunto "Fleet of UAVs"
Zagar, Maxime. "Set-membership estimation and distributed control for a fleet of UAVs for target search and tracking". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST164.
Texto completo da fonteThis thesis addresses the problem of searching and tracking an unknown number of mobile targets spread within an unknown Region of Interest (RoI) using a fleet of cooperating Unmanned Aerial Vehicles (UAVs). Such Cooperative Search, Acquisition, and Tracking (CSAT) problem appears in military contexts, e.g., when enemy vehicles have to be found and tracked, or in civilian applications, e.g., when searching for lost people after a disaster. To solve the CSAT problem, each UAV embeds a Computer Vision System (CVS) consisting of a camera and image processing algorithms that provide measurements related to the RoI. The CVS measurements consist of images with labeled pixels, depth maps, and boxes in the images containing pixels related to the detected and identified targets. This thesis considers a set-membership approach to address the CSAT problem. Compared to alternative approaches relying on stochastic assumptions on the measurement noise, set-membership approaches assume bounded measurement noise with known bounds. Set-membership approaches can then characterize sets that are guaranteed to contain the location of the targets, provided that the hypotheses on the measurement models and noise bounds are satisfied. Very few previous works directly exploit CVS measurements in a set-membership approach. This is mainly because obtaining measurement models for a CVS involving deep learning algorithms is difficult. To address these issues, we introduce several assumptions to relate the CVS measurements with the targets and obstacles present in the RoI using a geometric approach. With these assumptions, we propose a new set-membership estimator that directly exploits the CVS measurements to characterize sets that are guaranteed to contain the location of each identified target. The CVS measurements are also exploited to evaluate sets that are guaranteed to contain no target location. A prediction-correction scheme similar to the Kalman filter has been considered to account for the exchange of information between UAVs. The correction involves CVS measurements acquired by each UAV and estimates shared by neighboring UAVs. Several additional sources of uncertainty may be considered in the proposed approach. We have focused on the state uncertainty of UAVs. As the RoI is cluttered with unknown obstacles, each UAV builds an occupancy-elevation map during the search and tracking of targets using CVS measurements. The map provides an approximate description of the location, height, and shape of the obstacles. The map may be exploited for obstacle avoidance. In this thesis, it is used to predict the occlusion by obstacles in the field of view of each UAV. This information is instrumental in the design of a Model Predictive Control (MPC) algorithm to determine the trajectory of each UAV, minimizing the localization uncertainty of identified targets and reducing the size of the set containing potentially undetected targets. If the targets outnumber the UAVs, a trade-off has to be found between searching for new targets and tracking those already identified to reduce the localization uncertainty. Simulations performed with Webots illustrate the performance of the target location estimator and the MPC design by evaluating the efficiency of the cooperating UAVs to explore the environment, find and identify the targets, and maintain an accurate estimation of their location
Bardin, Jeremy, Dan Covelli, James Malvasio, Matt Olson, Daniel Speer e Orion Team Consulting. "BAMS UAS Manning and Fleet Integration Strategy". Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/7059.
Texto completo da fonteEXECUTIVE SUMMARY: The Persistent Maritime Unmanned Aircraft Systems Program Office (PMA-262) has an expected initial operational capability (IOC) in FY2014 for the Broad Area Maritime Surveillance Unmanned Aerial System (BAMS UAS). Prior to IOC and in anticipation of operational testing with Air Test and Evaluation Squadron-One (VX-1), Orion Team Consulting (OTC) has been tasked to provide CAPT Kenney, Commanding Officer VX-1, a best-value course of action (COA) for manning and Fleet integration of the BAMS UAS. OTC used the following question to drive research and data analysis to provide a cogent plan for the future manning of the UAS: Considering both manning cost and capability, which of the three COAs listed below is the best manning strategy for Fleet-wide employment of the BAMS UAS? 1. BAMS UAS Standalone Squadron 2. Joint BAMS / Global Hawk UAS Squadron 3. Combined BAMS UAS / P-8A Squadron OTC employed current and available manpower data, baseline BAMS UAS operational data, and existing Global Hawk (GH) UAS manpower and operational data to analyze the three courses of action (COAs). Additionally, OTC utilized the current BAMS UAS Manpower Business Case Analysisi We recommend that BAMS UAS Fleet integration be accomplished via a combined BAMS UAS / P-8A squadron. This recommendation has been determined by accounting for manning cost and capabilities. The preponderance of reasons for this recommendation is centered on the manning cost saving over the other two COAs coupled with an increase in organic capabilities and ability to leverage intelligence sharing/support. This would effectively augment the maritime ISR capabilities of the P-8A Poseidon, provide best-value accounting for capabilities, and allow for career (MBCA) as the benchmark for determining the best-value COA for manning and Fleet integration. This report will provide insight into the essential data utilized, key assumptions made, and the final recommendations derived through extensive research and coordination with the USAF 303rd Aeronautical Systems Group (AESG) – the Global Hawk UAS program office, and PMA-262. We recommend that BAMS UAS Fleet integration be accomplished via a combined BAMS UAS / P-8A squadron. This recommendation has been determined by accounting for manning cost and capabilities. The preponderance of reasons for this recommendation is centered on the manning cost saving over the other two COAs coupled with an increase in organic capabilities and ability to leverage intelligence sharing/support. This would effectively augment the maritime ISR capabilities of the P-8A Poseidon, provide best-value accounting for capabilities, and allow for career progression for the BAMS UAS air vehicle operators (AVOs), mission coordinators (MSN COORD), sensor operators, and maintainers.
Bellingham, John Saunders 1976. "Coordination and control of UAV fleets using mixed-integer linear programming". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/82252.
Texto completo da fonteSaif, Osamah. "Reactive navigation of a fleet of drones in interaction". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2269/document.
Texto completo da fonteNowadays, applications of autonomous quadrotors are increasing rapidly. Surveillance and security of industrial sites, geographical zones for agriculture for example are some popular applications of Unmanned Aerial Vehicles (UAVs). Nowadays, researchers and scientists focus on the deployment of multi-UAVs for the inspection and the surveillance of large areas. The objective of this thesis is to design algorithms and techniques to perform a real-time distributed/decentralized multi-UAVs flight formation control, from a system of systems perspective. Firstly, we reviewed recent works of the literature about flight formation control and the control of quadrotors. We presented a brief introduction about systems of systems, their definition and characteristics. Then, we introduced the flight formation control with its most used structures in the literature, some existing works dealing with flocking. Finally, we presented the most used modeling methodologies for quadrotors and some control approaches that are used to stabilize quadrotors. Secondly, we used the behavioral-based control structure to achieve a multiple UAV flocking. We conceived a behavior intending to address the control design towards a successful achievement of the flocking task without fragmentation. The proposed behavior treats the flocking problem from a global perspective, that is, we included a tendency of separated UAVs to form a flock.System of systems challenges motivated us to look for flocking and consensus algorithms introduced in the literature that could be helpful to answer to these challenges. This led us to propose four flocking control laws aiming at being compatible with the nonlinear model of quadrotors and at being implemented on experimental platforms. The control laws were run aboard each quadrotor in the flock. By running the control law, each quadrotor interacts with its neighbors to ensure a collision-free flocking. Finally, we validated our proposed control laws by simulations and real-time experiments. For the simulation, we used a PC-based simulator of flock of multiple quadrotors which was developed at Heudiasyc laboratory. For experiments, we implemented our control laws on ArDrone2 quadrotors evolved in an indoor environment equipped with an Optitrack motion capture system
Bailon-Ruiz, Rafael. "Design of a wildfire monitoring system using fleets of Unmanned Aerial Vehicles". Thesis, Toulouse, INSA, 2020. http://www.theses.fr/2020ISAT0011.
Texto completo da fonteWildfires, also known as forest or wildland fires, are uncontrolled vegetation fires occurring in rural areas that cause tremendous damage to the society, harming environment, property and people. The firefighting endeavor is a dull, dirty and dangerous job and as such, can greatly benefit from automation to reduce human exposure to hazards. Aerial remote sensing is a common technique to obtain precise information about a wildfire state so fire response teams can prepare countermeasures. This task, when performed with manned aerial vehicles, expose operators to high risks that can be eliminated by the use of autonomous vehicles. This thesis introduces a wildfire monitoring system based on fleets of unmanned aerial vehicles (UAVs) to provide firefighters with timely updated information about a wildland fire. We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind, to predict wildfire spread and plan UAV motion. The approach tailors a generic Variable Neighborhood Search method to these models and the associated constraints. The execution of the planned monitoring mission provides wildfire maps that are transmitted to the fire response team and exploited by the planning algorithm to plan new observation trajectories. Algorithms and models are integrated within a software architecture allowing for execution under scenarios with different levels of realism, with real and simulated UAVs flying over a real or synthetic wildfire. Mixed-reality simulation results show the ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model
Dietrich, Thomas [Verfasser], Armin [Akademischer Betreuer] Zimmermann, Andreas [Gutachter] Mitschele-Thiel e Jean-Yves [Gutachter] Choley. "Energy-efficient resource management for continuous scenario fulfillment by UAV fleets / Thomas Dietrich ; Gutachter: Andreas Mitschele-Thiel, Jean-Yves Choley ; Betreuer: Armin Zimmermann". Ilmenau : TU Ilmenau, 2019. http://d-nb.info/1200353536/34.
Texto completo da fonteCHIEN, WEI-CHEN, e 簡偉臣. "A Study of Using A Fleet of UAV to Spray Pesticides in The Rice Field". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nbvae8.
Texto completo da fonte國立雲林科技大學
工業工程與管理系
107
Rice is a staple Chinese food which is the largest crop in Taiwan. It is necessary to spray pesticides immediately for preventing the spread of pests and diseases. Nowadays, the unmanned aerial vehicles (UAVs) are invented to carry out pesticide spraying operations. It is also necessary to consider that the UAVs need to refill the pesticide and change the battery in a large field. This operation can be investigated as an agricultural field operations problem (AFOP) which is the major topic in this study. This study transforms the AFOP into a capacitated vehicle routing problem (CVRP). The goal of this study is to find an efficient method for arranging UAVs routing, refilling pesticides operation, and schedule of changing the battery. A mixed integer linear program mathematical model is developed to minimize the maximum operating time of UAVs. The overall operation time includes: (1) travel time, (2) operation time, (3) refilling time, and (4) changing battery time. A mathematical model is developed first. The solution algorithm is then developed by using the logic of Mix-opt simulated annealing algorithm. The solution program using Python language is also developed to solve a real-world problem. The Taguchi method is used to find better parameters used in the solution algorithm. Finally, a sensitivity analysis is conducted including: (1) adjust the UAVs spraying direction, (2) adjust the number of spraying UAVs, and (3) adjust the UAVs spraying rate. Based on the results of sensitivity analysis, suggestions and conclusions are made to provide the management level of agricultural operations.
HSU, HUNG-MING, e 許宏銘. "A Study of Multi-Objective Genetic Algorithm Applying on Routing Problem of Heterogeneous UAV Fleet with Adjustable View Angle". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/vw6fkt.
Texto completo da fonte中華大學
資訊工程學系
105
Unmanned Aerial Vehicle (UAV) with the characteristic of low cost and high mobility. Compared to flying traditional aircraft, the requirements of landing UAV site is low, the skill of UAV operation is ease, and UAV is capable to avoid the risk of human accident. Therefore, it is very suitable for alternative personnel to carry out a variety kinds of high-risk missions. Among these missions, aerial photography is one of the most popular application of unmanned aerial vehicles. In this paper, we investigated the uses of heterogeneous UAVs in aerial photography mission within a limited area. In the literature, numerous approaches have been proposed in UAV path planning, such as adding UAV flight parameter control, single or multiple UAVs flight path planning ... and so on. However, none of them discussed heterogeneous UAVs. In this paper, the concept of flying path is not equivalent to the cruise path is proposed. The objective of UAV path planning should meet the requirement of aerial photography mission while optimizes the mission costs. Therefore, we investigated the heterogeneous UAVs with adjustable image angle in aerial photography mission. A multiobjective optimization problem is formulated, considering the coverage of UAV camera, flight distance of UAVs, energy consumption, and the configuration of heterogeneous UAVs. In this paper, we proposed a multi-objective evolutionary approach to solve the investigated problem. From the experimental results, the proposed approach can effectively obtain multiple non-dominated solutions. The solutions also shows that the decoded routes of UAVs path is very similar to the appearance of the demand model.
Livros sobre o assunto "Fleet of UAVs"
Gilmore, Christopher, Michael Chaykowsky e Brent Thomas. Autonomous Unmanned Aerial Vehicles for Blood Delivery: A UAV Fleet Design Tool and Case Study. RAND Corporation, 2019. http://dx.doi.org/10.7249/rr3047.
Texto completo da fonteThomas, Brent, Christopher K. Gilmore e Michael Chaykowsky. Autonomous Unmanned Aerial Vehicles for Blood Delivery: A UAV Fleet Design Tool and Case Study. RAND Corporation, The, 2020.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Fleet of UAVs"
Radzki, Grzeogorz, Peter Nielsen, Amila Thibbotuwawa, Grzegorz Bocewicz e Zbigniew Banaszak. "Declarative UAVs Fleet Mission Planning: A Dynamic VRP Approach". In Computational Collective Intelligence, 188–202. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_15.
Texto completo da fonteGuo, Qing, Zhengzhong Xiang, Jian Peng e WenZheng Xu. "Data Collection of IoT Devices with Different Priorities Using a Fleet of UAVs". In Wireless Algorithms, Systems, and Applications, 218–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19214-2_18.
Texto completo da fonteThibbotuwawa, Amila, Peter Nielsen, Grzegorz Bocewicz e Zbigniew Banaszak. "UAVs Fleet Mission Planning Subject to Weather Fore-Cast and Energy Consumption Constraints". In Advances in Intelligent Systems and Computing, 104–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13273-6_11.
Texto completo da fonteZaitseva, Elena, Vitaly Levashenko, Nicolae Brinzei, Andriy Kovalenko, Marina Yelis, Viktors Gopejenko e Ravil Mukhamediev. "Reliability Assessment of UAV Fleets". In Emerging Networking in the Digital Transformation Age, 335–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24963-1_19.
Texto completo da fonteAkram, Raja Naeem, Konstantinos Markantonakis, Keith Mayes, Pierre-François Bonnefoi, Amina Cherif, Damien Sauveron e Serge Chaumette. "A Secure and Trusted Channel Protocol for UAVs Fleets". In Information Security Theory and Practice, 3–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93524-9_1.
Texto completo da fonteRadzki, G., P. Nielsen, G. Bocewicz e Z. Banaszak. "UAV Fleet Mission Planning Subject to Robustness Constraints". In Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, 35–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53829-3_4.
Texto completo da fonteZhou, Zhenyu, e Yanchao Liu. "A Scalable Cloud-Based UAV Fleet Management System". In Lecture Notes in Mechanical Engineering, 203–18. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17629-6_22.
Texto completo da fonteTožička, Jan, Benedek Szulyovszky, Guillaume de Chambrier, Varun Sarwal, Umar Wani e Mantas Gribulis. "Application of Deep Reinforcement Learning to UAV Fleet Control". In Advances in Intelligent Systems and Computing, 1169–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_85.
Texto completo da fonteAltshuler, Yaniv, Alex Pentland e Alfred M. Bruckstein. "Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs". In Swarms and Network Intelligence in Search, 207–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63604-7_8.
Texto completo da fonteKliushnikov, Ihor, Vyacheslav Kharchenko e Herman Fesenko. "UAV Fleet Routing with Battery Recharging for Nuclear Power Plant Monitoring Considering UAV Failures". In Communications in Computer and Information Science, 442–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14841-5_29.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Fleet of UAVs"
Karpstein, Robin, Victor Luis e Florian Holzapfel. "Agent-based Simulation of UAV based Logistics Networks with Real World Data". In Vertical Flight Society 80th Annual Forum & Technology Display, 1–16. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1407.
Texto completo da fonteDing, Jianing, Hai-Tao Zhang e Bin-Bin Hu. "Coordinated Landing Control for Cross-Domain UAV-USV Fleets Using Heterogeneous-Feature Matching". In 2024 IEEE International Conference on Robotics and Automation (ICRA), 12041–47. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611392.
Texto completo da fontePaudel, Saroj, Jiangfeng Zhang, Beshah Ayalew e Annette Skowronska. "Charging Load Estimation for a Fleet of Autonomous Vehicles". In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2025.
Texto completo da fonteKopyt, Antoni, Janusz P. Narkiewicz, Tomasz Malecki e Pawel Radyisyewski. "Optimal Object Location by a Fleet of Various UAVs". In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-2332.
Texto completo da fonteBailon-Ruiz, Rafael, Simon Lacroix e Arthur Bit-Monnot. "Planning to Monitor Wildfires with a Fleet of UAVs". In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593859.
Texto completo da fonteD'Amato, Egidio, Immacolata Notaro, Barbara Iodice, Giulia Panico e Luciano Blasi. "Decentralized Moving Horizon Estimation for a Fleet of UAVs". In 2022 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2022. http://dx.doi.org/10.1109/icuas54217.2022.9836138.
Texto completo da fonteKaneko, Shugo, e Joaquim R. Martins. "Fleet Design of Package Delivery UAVs Considering the Operations". In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-1503.
Texto completo da fonteBodin, François, Tristan Charrier, Arthur Queffelec e François Schwarzentruber. "Generating Plans for Cooperative Connected UAVs". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/846.
Texto completo da fonteVerdu, Titouan, Gautier Hattenberger e Simon Lacroix. "Flight patterns for clouds exploration with a fleet of UAVs". In 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2019. http://dx.doi.org/10.1109/icuas.2019.8797953.
Texto completo da fonteBelkadi, A., L. Ciarletta e D. Theilliol. "UAVs fleet control design using distributed particle swarm optimization: A leaderless approach". In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016. http://dx.doi.org/10.1109/icuas.2016.7502679.
Texto completo da fonte