Relevant bibliographies by topics / Unmanned ground vehicle

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

Academic literature on the topic 'Unmanned ground vehicle'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Unmanned ground vehicle"

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Juhari, Khairul Anuar, Mohd Rizal Salleh, Mohd Nazrin Muhamad, and Teruaki Ito. "208 NAVIGATION SYSTEM FOR UNMANNED GROUND VEHICLE." Proceedings of Manufacturing Systems Division Conference 2013 (2013): 53–54. http://dx.doi.org/10.1299/jsmemsd.2013.53.

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Hay, A., C. Samson, L. Tuck, and A. Ellery. "Magnetic surveying with an unmanned ground vehicle." Journal of Unmanned Vehicle Systems 6, no. 4 (December 1, 2018): 249–66. http://dx.doi.org/10.1139/juvs-2018-0013.

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With the recent proliferation of unmanned aerial vehicles for geophysical surveying, a novel opportunity exists to develop unmanned ground vehicles in parallel. This contribution features a study to integrate the Husky A200 robotic development platform with a GSMP 35U magnetometer that has recently been developed for the unmanned aerial vehicle market. Methods to identify and reduce the impact of magnetically noisy components on the unmanned ground vehicle platforms are discussed. The noise generated by the platform in laboratory and gentle field conditions, estimated using the fourth difference method for a magnetometer–vehicle separation distance of 121 cm and rotation of the chassis wheels at full speed (1 m/s), is ±1.97 nT. The integrated unmanned ground vehicle was used to conduct two robotic magnetic surveys to map cultural targets and natural variations of the magnetic field. In realistic field conditions, at a full speed of 1 m/s, the unmanned ground vehicle measured total magnetic intensity over a range of 1730 nT at 0.1 m spatial resolution with a productivity of 2651 line metres per hour.
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Peterson, John, Weilin Li, Brian Cesar‐Tondreau, John Bird, Kevin Kochersberger, Wojciech Czaja, and Morgan McLean. "Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search." Journal of Field Robotics 36, no. 4 (March 12, 2019): 818–45. http://dx.doi.org/10.1002/rob.21867.

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Typiak, Andrzej, and Michał Gnatowski. "Map Building System for Unmanned Ground Vehicle." Solid State Phenomena 180 (November 2011): 131–36. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.131.

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A typical unmanned and remotely operated vehicle is usually equipped with cameras which give insufficient information about the nearest environment and an operator has difficulties in driving such a vehicle in unknown environment. In this paper, we consider a problem of the vehicle nearest area map building based on additional devices. The vehicle is equipped with SICK LMS lasers, inclinometer and radars. Combining information from the devices allows to build a map which helps an operator to drive the vehicle more efficiently. We tested the system on a few military vehicles and the results show that our system really improves remotely driving.
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Biswas, Tanmoy, Arup Kr Goswami, Manas Pal, and Saptarshi Naskar. "SMS Controlled Unmanned Ground Vehicle." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 847–54. http://dx.doi.org/10.26438/ijcse/v6i12.847854.

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Fernandez, S. George, K. Vijayakumar, R. Palanisamy, K. Selvakumar, D. Karthikeyan, D. Selvabharathi, S. Vidyasagar, and V. Kalyanasundhram. "Unmanned and autonomous ground vehicle." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4466. http://dx.doi.org/10.11591/ijece.v9i5.pp4466-4472.

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Unmanned and Autonomous Ground Vehicle (UAGV) is a smart vehicle that capable of doing tasks without the need of human operator. The automated vehicle can work during off and on road navigation and also used in military operation such as detecting bombs, border patrol, carrying cargos, search, rescue etc reducing soldier’s exposure to danger, freeing them to perform other duties. This type of vehicle mainly uses sensors to observe the environment and automatically take decisions on its own in unpredictable situation and with unknown information or pass this information to the operator who control the UAGV through various communication when it requires support. This UAGV can send visual feedbacks to the operator at the ground station. An onboard sensor gives the complete environment of the vehicle as signals to the operator.
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Chang, Bao Rong, Hsiu-Fen Tsai, Jyong-Lin Lyu, and Chien-Feng Huang. "Distributed sensing units deploying on group unmanned vehicles." International Journal of Distributed Sensor Networks 17, no. 7 (July 2021): 155014772110368. http://dx.doi.org/10.1177/15501477211036877.

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This study aims to use two unmanned vehicles (aerial vehicles and ground vehicles) to implement multi-machine cooperation to complete the assigned tasks quickly. Unmanned aerial/ground vehicles can call each other to send instant inquiry messages using the proposed cooperative communication protocol to hand over the tasks between them and execute efficient three-dimensional collaborative operations in time. This study has demonstrated integrating unmanned aerial/ground vehicles into a group through the control platform (i.e. App operation interface) that uses the Internet of Things. Therefore, pilots can make decisions and communicate through App for cooperative coordination, allowing a group of unmanned aerial/ground vehicles to complete the tasks flexibly. In addition, the payload attached to unmanned air/ground vehicles can carry out multipurpose monitoring that implements face recognition, gas detection, thermal imaging, and video recording. During the experiment of unmanned aerial vehicle, unmanned aerial vehicle will plan the flight path and record the movement trajectory with global positioning system when it is on duty. As a result, the accuracy of the planned flight path achieved 86.89% on average.
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Liu, Qi, Zirui Li, Shihua Yuan, Yuzheng Zhu, and Xueyuan Li. "Review on Vehicle Detection Technology for Unmanned Ground Vehicles." Sensors 21, no. 4 (February 14, 2021): 1354. http://dx.doi.org/10.3390/s21041354.

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.
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Bhandari, Renuka, Vijaya Khati, Sangita Yadav, and Moni Kumari. "Wireless Network for Unmanned Ground Vehicle." INTERNATIONAL JOURNAL OF RESEARCH IN ADVANCE ENGINEERING 2, no. 3 (May 11, 2016): 21. http://dx.doi.org/10.26472/ijrae.v2i3.51.

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Zhang, Xin, Yan An Zhao, Li Gao, and Dong Hao Hao. "Evaluation Framework and Method of the Intelligent Behaviors of Unmanned Ground Vehicles Based on AHP Scheme." Applied Mechanics and Materials 721 (December 2014): 476–80. http://dx.doi.org/10.4028/www.scientific.net/amm.721.476.

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In order to provide basis and standards to the research on unmanned driving behaviors, a more thorough evaluation system of Intelligent Behavior for Unmanned Ground Vehicles needs to come forward. The intelligent behavior of unmanned ground vehicles in pedestrian crossing scenario is taken as an example in this paper. By using building and analyzing evaluation index system, this paper proposes an evaluation method that can comprehensively expressed the technological performance of unmanned ground vehicles based on Analytic Hierarchy Process (AHP). Compared with traditional methods, this evaluation method takes index weight into sufficient conderations and is more objective. The method properly works out with index weight of specific scenarios that reflects the actual situations. The establishment of comprehensive scoring method objectively and conveniently turns the performances of unmanned ground vehicle into scores, so that the results can be compared and ranked directly. Last but not least, a certain participating vehicle is used for case study. The result proves aforementioned method to be practical, reliable, convenient and logical. It not only evaluates the assessment comprehensively, but also evaluates the index separately to guide researchers to find out the defects of unmanned driving vehicle evaluation indexes and point out ways to improve them.
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Dissertations / Theses on the topic "Unmanned ground vehicle"

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Chen, Yuanyan. "Autonomous Unmanned Ground Vehicle (UGV) Follower Design." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1470951910.

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Teresi, Michael Bryan. "Multispectral Image Labeling for Unmanned Ground Vehicle Environments." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/53998.

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Described is the development of a multispectral image labeling system with emphasis on Unmanned Ground Vehicles(UGVs). UGVs operating in unstructured environments face significant problems detecting viable paths when LIDAR is the sole source for perception. Promising advances in computer vision and machine learning has shown that multispectral imagery can be effective at detecting materials in unstructured environments [1][2][3][4][5][6]. This thesis seeks to extend previous work[6][7] by performing pixel level classification with multispectral features and texture. First the images are spatially registered to create a multispectral image cube. Visual, near infrared, shortwave infrared, and visible/near infrared polarimetric data are considered. The aligned images are then used to extract features which are fed to machine learning algorithms. The class list includes common materials present in rural and urban scenes such as vehicles, standing water, various forms of vegetation, and concrete. Experiments are conducted to explore the data requirement for a desired performance and the selection of a hyper-parameter for the textural features. A complete system is demonstrated, progressing from the data collection and labeling to the analysis of the classifier performance.
Master of Science
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Wagner, Anthony Julian. "Online Unmanned Ground Vehicle Mission Planning using Active Aerial Vehicle Exploration." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90785.

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This work presents a framework for the exploration and path planning for a collaborative UAV and UGV system. The system is composed of a UAV with a stereo system for obstacle detection and a UGV with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the UAV. Both identify frontiers for exploration with the Dijkstra Frontier method using Dijkstra's Algorithm to identify a frontier with unknown space, and the other uses a bi-directional RRT to identify multiple frontiers for selection. The final algorithm developed was for to give the UGV partial plans when an entire plan is not yet found. This improves the overall mission tempo. The algorithm is designed to keep the UGV a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in Gazebo using the ROS framework. The Dijkstra Frontier method was also tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The UGV partial plan method showed a decreased traveled distance for the UGV but increases in UGV mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions.
Master of Science
This work presents a framework for the exploration and path planning for a collaborative aerial and ground vehicle robotic system. The system is composed of an aircraft with a camera system for obstacle detection and a ground vehicle with no sensors for obstacle detection. Two exploration algorithms were developed to guide the exploration of the aircraft. Both identify frontiers for exploration with the Dijkstra Frontier method using path planning algorithms to identify a frontier with unknown space (Dijkstra Frontier), and the other uses a sampling based path planning method (RRT Explore) to identify multiple frontiers for selection. The final algorithm developed was for to give the ground vehicle intermediate plans when an entire plan is not yet found. The algorithm is designed to keep the ground vehicle a safe distance from the unknown frontier to prevent backtracking. All the algorithms were tested in a simulation framework using Robot Operating System and one exploration method was tested on the hardware system. The results show the RRT Explore algorithm to work well for exploring the environment, performing equally or better than the Dijkstra Frontier method. The ground vehicle intermediate plan method showed a decreased traveled distance for the ground vehicle but increases in ground vehicle mission time with more conservative distances from danger. Overall, the framework showed a good exploration of the environment and performs the intended missions.
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Kirsch, Patricia Jean. "Autonomous swarms of unmanned vehicles software control system and ground vehicle testing /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2993.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Källström, Alexander, and Jagesten Albin Andersson. "Autonomous Landing of an Unmanned Aerial Vehicle on an Unmanned Ground Vehicle in a GNSS-denied scenario." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167924.

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An autonomous system consisting of an unmanned aerial vehicle (UAV) in cooperation with an unmanned ground vehicle (UGV) is of interest in applications for military reconnaissance, surveillance and target acquisition (RSTA). The basic idea of such a system is to take advantage of the vehicles strengths and counteract their weaknesses. The cooperation aspect suggests that the UAV is capable of autonomously landing on the UGV. A fundamental part of the landing is to localise the UAV with respect to the UGV. Traditional navigation systems utilise global navigation satellite system (GNSS) receivers for localisation. GNSS receivers have many advantages, but they are sensitive to interference and spoofing. Therefore, this thesis investigates the feasibility of autonomous landing in a GNSS-denied scenario. The proposed landing system is divided into a control and an estimation system. The control system uses a proportional navigation (PN) control law to approach the UGV. When sufficiently close, a proportional-integral-derivative (PID) controller is used to match the movements of the UGV and perform a controlled descent and landing. The estimation system comprises an extended Kalman filter that utilises measurements from a camera, an imu and ultra-wide band (UWB) impulse radios. The landing system is composed of various results from previous research. First, the sensors used by the landing system are evaluated experimentally to get an understanding of their characteristics. The results are then used to determine the optimal sensor placements, in the design of the EKF, as well as, to shape the simulation environment and make it realistic. The simulation environment is used to evaluate the proposed landing system. The combined system is able to land the UAV safely on the moving UGV, confirming a fully-functional landing system. Additionally, the estimation system is evaluated experimentally, with results comparable to those obtained in simulation. The overall results are promising for the possibility of using the landing system with the presented hardware platform to perform a successful landing.
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Berglund, Daniel. "Development of an Unmanned Ground Vehicle (UGV) user interface." Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80419.

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Examensarbetet syftar till att konstruera operatörsgränssnittet till en UGV (eng Unmanned Ground Vehicle) för mobil rekognosering i tätbebyggt område. Operatörsgränssnittet består av både mjuk- och hårdvara. Till operatörsgränssnittet har ett grafiskt användargränssnitt utvecklats där tonvikten legat på användbarhet med funktionalitet för positionering med geografisk presentation och sensorpresentation. Förutom en presentationsenhet för sensorinteraktion och manövrering av farkost ingår en sändtagarenhet för kommunikation mellan operatör och farkost. Två uppsättningar av systemet levererades till försvarsmakten för användning vid metodförsöksstudier.
The purpose of this thesis is to develop the user interface for a UGV (Unmanned Ground Vehicle) intended for mobile reconnaissance in urban areas. The operator interface consists of both soft- and hardware. As a part, a graphical user interface has been developed with emphasis on usability including functionality for positioning with geographical presentation as well as sensor presentation. In addition to a display unit for sensor interaction and manoeuvring of the vehicle, the user interface includes a transceiver unit that handles the operator and vehicle intercommunication. Two sets of the system were delivered to the Swedish armed forces to be used in trials.
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Al-Mayyahi, Auday Basheer Essa. "Motion control of unmanned ground vehicle using artificial intelligence." Thesis, University of Sussex, 2018. http://sro.sussex.ac.uk/id/eprint/76665/.

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The aim of this thesis is to solve two problems: the. trajectory tracking and navigation, for controlling the motion of unmanned ground vehicles (UGV). Such vehicles are usually used in industry for assisting automated production process or delivery services to improve and enhance the quality and efficiency. With regard to the trajectory tracking problem, the main task is to design a new method that is capable of minimising trajectory-tracking errors in UGV. To achieve this, a comprehensive mathematical model needs to be established that contains kinematic and dynamic characteristics beside actuators. In addition, different trajectories need to be generated and applied individually as a reference input, i.e. continuous gradient trajectories such as linear, circular and lemniscuses or a non-continuous gradient trajectory such as a square trajectory. The design method is based on a novel fractional order proportional integral derivative (FOPID) control strategy, which is proposed to control the movement of UGV to track given trajectories. Two FOPID controllers are required in this design. The first FOPID is constructed in order to control the orientation of UGV. The second FOPID controller is to control the speed of UGV. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters for both controllers. The significance of the proposed method is that an observable improvement has been achieved in terms of minimising trajectory-tracking errors and reducing control efforts, especially in continuous gradient trajectories. The stability of the proposed controllers is investigated based upon Nyquist stability criterion. Moreover, the robustness of the controllers is examined in the presence of disturbances to demonstrate the effectiveness of the controllers under certain harsh conditions. The influence from external disturbances has been represented by square pulses and sinusoidal waves. The drawback of this method, however, a highly trajectory tracking error is observed in non-continuous gradient trajectories due to the sharpness of the rotation at the corners of a square trajectory. To overcome this drawback, a new controller, abbreviated as (NN-FOPID), has been proposed based on a combination of neural networks and the FOPID. The purpose is to minimise the trajectory tracking error of non-continuous trajectories, in particular. The Levenberg-Marquardt (LM) algorithm is used to train the NN-FOPID controller. The neural networks' cognitive capacities have made the system adaptable to respond effectively to the variants in trajectories. The obtained results by using NN-FOPID have shown a significant improvement of reducing errors of trajectory tracking and increasing control efforts over the results by FOPID. The other task is to solve the navigation problem of UGV in static and dynamic environments. This can be conducted by firstly constructing workspace environments that contain multiple dynamic and static obstacles. The dynamic obstructing obstacles can move in different velocities. The static obstacles can be randomly positioned in the workspace and all obstacles are allowed to have different sizes and shapes. Secondly, a UGV can be placed in any initial posture on the condition that it has to reach a given destination within the boundaries of the workspace. Thirdly, a method based on fuzzy inference systems (FIS) is proposed to control the motion of the UGV. The design of FIS is based on fuzzification, inference engine and defuzzification processes. The navigation task is divided into obstacle avoidance and target reaching tasks. Consequently, two individual FIS controllers are required to drive the actuators of the UGV, one is to avoid obstacles and the other is to reach a target. Both FIS controllers are combined through a switching mechanism to select the obstacle avoidance FIS controller if there is an obstacle, otherwise choosing reaching target FIS. The simulation results have confirmed the effectiveness of the proposed design in terms of obtaining optimal paths with shortest elapsed time. Similarly, a new method is proposed based on an adaptive neurofuzzy inference system (ANFIS) to guide the UGV in unstructured environments. This method combines the advantages of adaptive leaning and inference fuzzy system. The simulation results have demonstrated adequate achievements in terms of obtaining shortest and feasible paths whilst avoiding static obstructing obstacles and hence reaching the specified targets speedily. Finally, a UGV is constructed to investigate the overall performance of the proposed FIS controllers practically. The architecture of the UGV consists of three ultrasonic sensors, a magnetic compass and two quadratic decoders that they are interfaced with an Arduino microcontroller to read the sensory information. The Arduino, who acts as a slave microcontroller is serially connected with a master Raspberry Pi microcontroller. Raspberry Pi and Arduino communicate with each other based on a proposed hierarchical algorithm. Three case studies are introduced to demonstrate the effectiveness and the validation of the proposed FIS controllers and the UGV's platform in real-time.
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Yu, Ada (Ada Cheuk Ying). "Design for manufacturing analysis on the Small Unmanned Ground Vehicle." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44849.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2008.
Includes bibliographical references (p. 65).
iRobot is responsible for delivering the Small Unmanned Ground Vehicle (SUGV) as part of the U.S. Army's Future Combat Systems (FCS) initiative. With increasing external competition and pressures, iRobot must deliver an innovative robot while reducing costs, improving quality, and shortening the product's time to market. Since 100% of iRobot's manufacturing is outsourced, the SUGV manufacturing team must optimize its mechanical design in order to help ensure a smooth handoff between its design team and its contract manufacturer. To achieve this goal, the SUGV manufacturing team utilized a Design for Manufacturability and Assembly (DFMA) analysis to simplify components, reduce assembly steps, and improve processes. This paper describes the benefits of DFMA and the tools and techniques used in conducting this analysis. By studying mechanical assemblies, reviewing design drawings with the engineers, and gathering best practices from other industries, this paper provides recommendations for design changes on the SUGV and organizational strategies that can help improve iRobot's product development process.
by Ada Yu.
S.M.
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Persson, Linnea. "Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187667.

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High Altitude Long Endurance (HALE) platforms are a type of Unmanned Aerial Vehicle (UAV). With their relatively easy deployment and independence of a fixed orbit, HALE UAVs have the potential to replace satellites for certain tasks in the future. A challenge with this technology is that the current platforms are too heavy to fly for a long period of time. A suggested method for reducing the weight is to remove the landing gear to instead use alternative methods for take-off and landing. One such alternative method is to land the UAV on top of a cooperating ground vehicle. In this thesis, the cooperative controller and the experimental setup of such a landing have been investigated. The cooperation between the systems was analyzed and evaluated analytically, through simulations and with flight tests. Using a PID controller for the position alignment and a modified flare law for the descent, feasibility of the landing was verified by performing a landing of a Penguin BE fixed-wing UAV on top of a cooperating ground vehicle.
Så kallade HALE - High Altitude Long Endurance -farkoster är en växande teknik inom området för autonoma flygplan. Med fördelar som exempelvis en möjlighet att röra sig oberoende av en omloppsbana samt en mer effektiv implementering– och utvecklingsprocess har de visat potential att i framtiden kunna ersätta satelliter inom vissa områden. Ett problem är i dagsläget svårigheten att bygga tillräckligt lätta farkoster för att kunna flyga under en längre tidsperiod. För att minska vikten har det bland annat föreslagits att landningsställ kan tas bort för att istället använda alternativa start- och landningsmetoder. I detta projekt har en metod undersökts där idén är att landa ett autonomt flygplan på en mobil plattform. Samarbetet mellan systemen har analyserats både analytiskt och genom tester. Slutligen verifieras att en kooperativ landning är genomförbar genom att en landning av ett obemannat flygplan på en samarbetande bil utförs.
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Beach, Timothy M. "Mobility modeling and estimation for delay tolerant unmanned ground vehicle networks." Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/34624.

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Approved for public release; distribution is unlimited
An ad hoc unmanned ground vehicle (UGV) network operates as an intermittently connected mobile delay tolerant network (DTN). The path planning strategy in a DTN requires mobility estimation of the spatial positions of the nodes as a function of time. The purpose of this thesis is to create a foundational mobility estimation algorithm that can be coupled with a cooperative communication routing algorithm to provide a basis for real time path planning in UGV-DTNs. In this thesis, we use a Gauss-Markov state space model for the node dynamics. The measurements are constant power received signal strength indicator (RSSI) signals transmitted from fixed position base stations. An extended Kalman filter (EKF) is derived for estimating of coordinates in a two-dimensional spatial grid environment. Simulation studies are conducted to test and validate the models and estimation algorithms. We simulate a single mobile node traveling along a trajectory that includes abrupt maneuvers. Estimation performance is measured using zero mean whiteness tests on the innovations sequences, root mean squared error (RMSE) of the state estimates, weighted sum squared residuals (WSSRs) on the innovations, and the posterior Cramer-Rao lower bound (PCRLB). Under these performance indices, we demonstrate that the mobility estimator performs effectively.
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Books on the topic "Unmanned ground vehicle"

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Cersovsky, Donald D. Mathematical model and analysis of the Tactical Unmanned Ground Vehicle (TUGV) using computer simulation. Monterey, Calif: Naval Postgraduate School, 1993.

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Hebert, Martial H., Charles Thorpe, and Anthony Stentz, eds. Intelligent Unmanned Ground Vehicles. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9.

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Hebert, Martial H. Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon. Boston, MA: Springer US, 1997.

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Introduction to unmanned systems: Air, ground, sea & space : technologies and commercial applications. [Phoenix, AZ]: Unmanned Vehicle University Press, 2013.

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Neta, Beny. Benefit of sound cueing in combat simulation. Monterey, Calif: Naval Postgraduate School, 1993.

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Hume, David B. Integration of weaponized unmanned aircraft into the air-to-ground system. Maxwell Air Force Base, Ala: Air University Press, 2007.

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Gerhart, Grant R. Unmanned Ground Vehicle Technology 7. SPIE-International Society for Optical Engine, 2005.

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Ma, Yue. Dynamics and Advanced Motion Control of Unmanned Ground off-Road Vehicle. Elsevier Science & Technology, 2020.

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R, Gerhart Grant, Gunderson Robert W, Shoemaker Chuck M, and Society of Photo-optical Instrumentation Engineers., eds. Unmanned ground vehicle technology II: 24-25 April, 2000, Orlando, USA. Bellingham, Wash: SPIE, 2000.

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Darpa. Rsta: Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground Vehicle. Morgan Kaufmann Publishers, 1997.

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Book chapters on the topic "Unmanned ground vehicle"

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Langer, Dirk, and Charles E. Thorpe. "Sonar-Based Outdoor Vehicle Navigation." In Intelligent Unmanned Ground Vehicles, 159–85. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_9.

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Huang, Jidong, and Michael Yeh. "The CSUF Unmanned Utility Ground Robotic Vehicle." In Intelligent Robotics and Applications, 344–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_35.

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Sezer, Volkan, Pınar Boyraz, Ziya Ercan, Çagri Dikilitaş, Hasan Heceoğlu, Alper Öner, Gülay Öke, and Metin Gökaşan. "Unmanned Ground Vehicle Otonobil: Design, Perception, and Decision Algorithms." In Smart Mobile In-Vehicle Systems, 47–56. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9120-0_4.

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Liao, Xiaohong, Zhao Sun, Liguo Weng, Bin Li, Yongduan Song, and Yao Li. "Adaptive Neural Network Path Tracking of Unmanned Ground Vehicle." In Advances in Neural Networks - ISNN 2006, 1233–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_179.

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Someshwaran, M., Deepa Jose, and P. Paul Jefferson. "Autonomous Unmanned Ground Vehicle for Enhancement of Defence Strategies." In Lecture Notes in Networks and Systems, 873–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0146-3_84.

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Hu, Qiuxia, Jin Zhao, and Lei Han. "Cooperative Path Planning for Intelligent Vehicle Using Unmanned Air and Ground Vehicles." In Lecture Notes in Electrical Engineering, 603–11. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_58.

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Zhao, Yibing, Jining Li, Linhui Li, and Hai Wang. "Obstacle Detection for Unmanned Ground Vehicle in Cross-Country Environment." In Advances in Mechanical and Electronic Engineering, 549–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31507-7_87.

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Ziebinski, Adam, Rafal Cupek, Marek Drewniak, and Bartlomiej Wolny. "Soft Real-Time Systems for Low-Cost Unmanned Ground Vehicle." In Computational Collective Intelligence, 196–206. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_17.

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Zoto, Jurgen, Maria Angela Musci, Aleem Khaliq, Marcello Chiaberge, and Irene Aicardi. "Automatic Path Planning for Unmanned Ground Vehicle Using UAV Imagery." In Advances in Service and Industrial Robotics, 223–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19648-6_26.

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Desai, Alok, Dah-Jye Lee, and Meng Zhang. "Using Accurate Feature Matching for Unmanned Aerial Vehicle Ground Object Tracking." In Advances in Visual Computing, 435–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14249-4_41.

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Conference papers on the topic "Unmanned ground vehicle"

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Ebken, John, Mike Bruch, and Jason Lum. "Applying unmanned ground vehicle technologies to unmanned surface vehicles." In Defense and Security, edited by Grant R. Gerhart, Charles M. Shoemaker, and Douglas W. Gage. SPIE, 2005. http://dx.doi.org/10.1117/12.605254.

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Janetka, M., L. Filz, N. Smith, and R. Frederick, Jr. "Unmanned air ground vehicle." In 37th Joint Propulsion Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-3433.

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Sinsley, Gregory, and Lyle Long. "Unmanned Aerial Vehicle and Unmanned Ground Vehicle Distributed Mapping." In AIAA Infotech@Aerospace 2010. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-3368.

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MacArthur, Donald K., and Carl D. Crane. "Unmanned Ground Vehicle State Estimation using an Unmanned Air Vehicle." In 2007 International Symposium on Computational Intelligence in Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/cira.2007.382909.

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Maheswaran, S., G. Murugesan, Prakash Duraisamy, B. Vivek, S. Selvapriya, S. Vinith, and V. Vasantharajan. "Unmanned Ground Vehicle for Surveillance." In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225313.

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Arrshith, R. G., K. S. Suhas, C. Tejas, and Ganapathy Subramaniyam. "Unmanned ground vehicle (UGV) — Defense bot." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8398995.

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Meldrum, Jay S., Christopher A. Green, Geoffrey D. Gwaltney, Scott A. Bradley, Jason M. Keith, and Thomas F. Podlesak. "Fuel-cell powered unmanned ground vehicle." In Defense and Security Symposium, edited by Grant R. Gerhart, Douglas W. Gage, and Charles M. Shoemaker. SPIE, 2007. http://dx.doi.org/10.1117/12.720791.

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Gupta, Varun, Amul Kumbhare, and Rita Jain. "Advanced Anti-Terrorism Unmanned Ground Vehicle." In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2018. http://dx.doi.org/10.1109/sceecs.2018.8546978.

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Hee Chang Moon, Kyoung Moo Min, and Jung Ha Kim. "Vision system of Unmanned Ground Vehicle." In 2008 International Conference on Control, Automation and Systems (ICCAS). IEEE, 2008. http://dx.doi.org/10.1109/iccas.2008.4694573.

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Ju-Jang Lee. "Unmanned technology for autonomous ground vehicle." In 2009 IEEE International Symposium on Industrial Electronics (ISIE 2009). IEEE, 2009. http://dx.doi.org/10.1109/isie.2009.5217917.

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Reports on the topic "Unmanned ground vehicle"

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COMPUTING TECHNOLOGIES INC DUMFRIES VA. Unmanned Ground Vehicle. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada406303.

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Ebken, John, Mike Bruch, and Jason Lum. Applying Unmanned Ground Vehicle Technologies To Unmanned Surface Vehicles. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada434099.

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Blackburn, M. R., R. T. Laird, and H. R. Everett. Unmanned Ground Vehicle (UGV) Lessons Learned. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada495124.

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Pacis, E. B., H. R. Everett, N. Farrington, G. Kogut, B. Sights, T. Kramer, M. Thompson, D. Bruemmer, and D. Few. Transitioning Unmanned Ground Vehicle Research Technologies. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada432516.

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ABERDEEN TEST CENTER MD. Testing of Unmanned Ground Vehicle (UGV) Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada500105.

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Harguess, Josh, and Shawn Strange. Infrared Stereo Calibration for Unmanned Ground Vehicle Navigation. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada607968.

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Thorpe, Charles, Martial Hebert, Dean Pomerleau, Anthony Stentz, and Takeo Kanade. Unmanned Ground Vehicle System Perception for Outdoor Navigation. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada352124.

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Nguyen, Hoa G., and Mendel Baker. Characteristics of a Maritime Interdiction Operations Unmanned Ground Vehicle. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada563950.

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Baca, Glenn. An Analysis of U.S. Army Unmanned Ground Vehicle Strategy. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada568455.

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Larson, Jacoby, Brian Okorn, Tracy Pastore, David Hooper, and Jim Edwards. Counter Tunnel Exploration, Mapping, and Localization with an Unmanned Ground Vehicle. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada607907.

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