Relevant bibliographies by topics / Unmanned ground vehicles

Contents

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

Academic literature on the topic 'Unmanned ground vehicles'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Unmanned ground vehicles"

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Gorsky, Alexander, Vitaliy Demyanov, and Alexander Zhukov. "Problem of creation ground robotics vehicle." Robotics and Technical Cybernetics 10, no. 2 (June 2022): 154–60. http://dx.doi.org/10.31776/rtcj.10209.

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The article discusses about the system of information support for unmanned ground vehicle and un-manned aerial vehicles with an increased degree of autonomy, arising application difficulties, is considered. Some methodological approaches to calculating the requirements of electronic protection for the data transmission systems of unmanned ground vehicle and unmanned aerial vehicles with an increased degree of autonomy are given.
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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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Li, Xin, Guang Ming Xiong, Yang Sun, Shao Bin Wu, Jian Wei Gong, Hui Yan Chen, and Li Gao. "Design on Hierarchical Testing System for Unmanned Ground Vehicles." Advanced Materials Research 346 (September 2011): 817–22. http://dx.doi.org/10.4028/www.scientific.net/amr.346.817.

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The test system for technical abilities of unmanned vehicles is gradually developed from the single test to comprehensive test. The pre-established test and evaluation system can promote the development of unmanned ground vehicles. The 2009 Future Challenge: Intelligent Vehicles and Beyond (FC’09) pushed China's unmanned vehicles out of laboratories. This paper proposed to design a more scientific and comprehensive test system for future competitions to better guide and regulate the development of China's unmanned vehicles. According to the design idea of stage by stage and level by level, the hierarchical test content from simple to advanced, from local to overall is designed. Then the hierarchic test environment is established according to the levels of test content. The test method based on multi-platform and multi-sensor is put forward to ensure the accuracy of test results. The testing criterion framework is set up to regulate future unmanned vehicle contests and to assess the unmanned vehicles scientifically and accurately.
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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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Ali, Ali M., Md Asri Ngadi, Rohana Sham, and Israa Ibraheem Al_Barazanchi. "Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach." Sensors 23, no. 3 (January 28, 2023): 1431. http://dx.doi.org/10.3390/s23031431.

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Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things.
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Al-Bkree, Mahmod. "Optimizing Perimeter Surveillance Drones to enhance the security system of unmanned aerial vehicles." Security science journal 2, no. 2 (December 13, 2021): 105–15. http://dx.doi.org/10.37458/ssj.2.2.7.

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This work is to optimize perimeter surveillance and explore the distribution of ground bases for unmanned aerial vehicles along the Jordanian border and optimize the set of technologies for each aerial vehicle. This model is part of ongoing research on perimeter security systems based on unmanned aerial vehicles. The suggested models give an initial insight about selecting technologies carried by unmanned aerial vehicles based on their priority; it runs for a small scale system that can be expanded, the initial results show the need for at least four ground bases along the length of the border, and a selected set of various technologies for each vehicle.
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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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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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Shipov, Il’ya, and Evgeniy Vetoshkin. "Integrated navigation of unmanned ground vehicles." Robotics and Technical Cybernetics 9, no. 2 (June 30, 2021): 127–32. http://dx.doi.org/10.31776/rtcj.9207.

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The article considers the experience of creating integrated navigation systems for ground robotic complexes. The main difficulties of choosing the instrument composition and element base in the conditions of domestic industry are outlined. A typical algorithm for prioritizing the initial data for the integrating and generating solutions algorithm for tasks of orientation and determining spatial position is described.
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Muangmin, Kamonwan, and Thanapat Wanichanon. "Formation keeping of unmanned ground vehicles." MATEC Web of Conferences 95 (2017): 09006. http://dx.doi.org/10.1051/matecconf/20179509006.

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Dissertations / Theses on the topic "Unmanned ground vehicles"

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Kirchhoff, Allan Richard. "Text Localization for Unmanned Ground Vehicles." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52569.

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Unmanned ground vehicles (UGVs) are increasingly being used for civilian and military applications. Passive sensing, such as visible cameras, are being used for navigation and object detection. An additional object of interest in many environments is text. Text information can supplement the autonomy of unmanned ground vehicles. Text most often appears in the environment in the form of road signs and storefront signs. Road hazard information, unmapped route detours and traffic information are available to human drivers through road signs. Premade road maps lack these traffic details, but with text localization the vehicle could fill the information gaps. Leading text localization algorithms achieve ~60% accuracy; however, practical applications are cited to require at least 80% accuracy [49]. The goal of this thesis is to test existing text localization algorithms against challenging scenes, identify the best candidate and optimize it for scenes a UGV would encounter. Promising text localization methods were tested against a custom dataset created to best represent scenes a UGV would encounter. The dataset includes road signs and storefront signs against complex background. The methods tested were adaptive thresholding, the stroke filter and the stroke width transform. A temporal tracking proof of concept was also tested. It tracked text through a series of frames in order to reduce false positives. Best results were obtained using the stroke width transform with temporal tracking which achieved an accuracy of 79%. That level of performance approaches requirements for use in practical applications. Without temporal tracking the stroke width transform yielded an accuracy of 46%. The runtime was 8.9 seconds per image, which is 44.5 times slower than necessary for real-time object tracking. Converting the MATLAB code to C++ and running the text localization on a GPU could provide the necessary speedup
Master of Science
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Umansky, Mark. "A Prototype Polarimetric Camera for Unmanned Ground Vehicles." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23724.

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Unmanned ground vehicles are increasingly employing a combination of active sensors such as LIDAR with passive sensors like cameras to perform at all levels of perception, which includes detection, recognition and classification. Typical cameras measure the intensity of light at a variety of different wavelengths to classify objects in different areas of an image. A polarimetric camera not only measures intensity of light, but can also determine its state of polarization. The polarization of light is the angle the electric field of the wave of light takes as it travels. A polarimetric camera can identify the state of polarization of the light, which can be used to segment highly polarizing areas in a natural environment, such the surface of water. The polarimetric camera designed and built for this thesis was created with low cost in mind, as commercial polarimetric cameras are very expensive. It uses multiple beam splitters to split incoming light into four machine vision cameras. In front of each machine vision camera is a linear polarizing filter that is set to a specific orientation. Using the data from each camera, the Stokes vector can be calculated on a pixel by pixel basis to determine what areas of the image are more polarized. Test images of various scenes that included running water, standing water, mud, and vehicles showed promise in using polarization data to highlight and identify areas of interest. This data could be used by a UGV to make more informed decisions in an autonomous navigation mode.
Master of Science
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Rodriguez, Uriel. "Miniaturization of ground station for unmanned air vehicles." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008480.

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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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Olsson, Martin. "Obstacle detection using stereo vision for unmanned ground vehicles." Thesis, Linköping University, Department of Science and Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18255.

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Henderson, Harold Paulk Bevly David M. "Relative positioning of unmanned ground vehicles using ultrasonic sensors." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Mechanical_Engineering/Thesis/Henderson_Harold_55.pdf.

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Daily, Robert L. Bevly David M. "Stream function path planning and control for unmanned ground vehicles." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SUMMER/Mechanical_Engineering/Dissertation/Daily_Robert_45.pdf.

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Bayar, Gokhan. "Trajectory Tracking Control Of Unmanned Ground Vehicles In Mixed Terrain." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615105/index.pdf.

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Mobile robots are commonly used to achieve tasks involving tracking a desired trajectory and following a predefined path in different types of terrains that have different surface characteristics. A mobile robot can perform the same navigation task task over different surfaces if the tracking performance and accuracy are not essential. However, if the tracking performance is the main objective, due to changing the characteristics of wheel-ground interaction, a single set of controller parameters or an equation of motion might be easily failing to guarantee a desired performance and accuracy. The interaction occurring between the wheels and ground can be integrated into the system model so that the performance of the mobile robot can be enhanced on various surfaces. This modeling approach related to wheel-ground interaction can also be incorporated into the motion controller. In this thesis study, modeling studies for a two wheeled differential drive mobile robot and a steerable four-wheeled robot vehicle are carried out. A strategy to achieve better tracking performance for a differential drive mobile robot is developed by introducing a procedure including the effects of external wheel forces
i.e, traction, rolling and lateral. A new methodology to represent the effects of lateral wheel force is proposed. An estimation procedure to estimate the parameters of external wheel forces is also introduced. Moreover, a modeling study that is related to show the effects of surface inclination on tracking performance is performed and the system model of the differential drive mobile robot is updated accordingly. In order to accomplish better trajectory tracking performance and accuracy for a steerable four-wheeled mobile robot, a modeling work that includes a desired trajectory generator and trajectory tracking controller is implemented. The slippage is defined via the slip velocities of steerable front and motorized rear wheels of the mobile robot. These slip velocities are obtained by using the proposed slippage estimation procedure. The estimated slippage information is then comprised into the system model so as to increase the performance and accuracy of the trajectory tracking tasks. All the modeling studies proposed in this study are tested by using simulations and verified on experimental platforms.
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Omelchenko, Alexander 1968. "Avionics systems design for cooperative unmanned air and ground vehicles." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17789.

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Thesis (S.M. and E.A.A.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.
"June 2004."
Includes bibliographical references (p. 95).
This thesis summarizes the results of the design of avionics systems intended for use onboard unmanned air and ground vehicles, that are parts of a multi-vehicle system whose primary mission objective is to provide up-close surveillance capability from a large stand-off distance. Different types of cooperative action between air and ground vehicles, that can help to enhance the overall system surveillance capability, are analyzed, including communication relay, simultaneous visual surveillance of ground objects from air and ground vehicles, and visual coverage of ground vehicles from air vehicles. Both hardware and software design as well as practical implementation of the designed avionics systems are discussed, and results of field tests are presented.
by Alexander Omelchenko.
S.M.and E.A.A.
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Spenko, Matthew J. (Matthew Julius) 1976. "Hazard avoidance for high-speed rough-terrain unmanned ground vehicles." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32389.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
"June 2005."
Includes bibliographical references (p. 111-116).
High-speed unmanned ground vehicles have important applications in rough-terrain. In these applications unexpected and dangerous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform navigation and hazard avoidance calculations based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot's performance due to model parameter and sensor uncertainty. This thesis presents the development and analysis of a novel method for high speed navigation and hazard avoidance. The method is based on the two dimensional "trajectory space," which is a compact model-based representation of a robot's dynamic performance limits on natural terrain. This method allows a vehicle to perform dynamically feasible hazard avoidance maneuvers in a computationally efficient manner. This thesis also presents a novel method for trajectory replanning, based on a "curvature matching" technique. This method quickly generates a path connects the end of the path generated by a hazard avoidance maneuver to the nominal desired path. Simulation and experimental results with a small gasoline-powered high-speed unmanned ground vehicle verify the effectiveness of these algorithms. The experimental results demonstrate the ability of the algorithm to account for multiple hazards, varying terrain inclination, and terrain roughness. The experimental vehicle attained speeds of 8 m/s (18 mph) on flat and sloped terrain and 7 m/s (16 mph) on rough terrain.
by Matthew J. Spenko.
Ph.D.
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Books on the topic "Unmanned ground vehicles"

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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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National Research Council (U.S.). Committee on Army Unmanned Ground Vehicle Technology. and National Research Council (U.S.). Board on Army Science and Technology., eds. Technology development for Army unmanned ground vehicles. Washington, D.C: National Academies Press, 2002.

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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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R, Gerhart Grant, Shoemaker Chuck M, Gage Douglas W. 1945-, and Society of Photo-optical Instrumentation Engineers., eds. Unmanned ground vehicle technology V: 22-23 April, 2003, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2003.

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

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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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Ni, Jun, Jibin Hu, and Changle Xiang. Design and Advanced Robust Chassis Dynamics Control for X-by-Wire Unmanned Ground Vehicle. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01496-3.

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Dubanov, Aleksandr. Computer simulation in pursuit problems. ru: Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. These methods are taken as the basis of the study and are modified in the future. The scientific novelty of the study is the iterative calculation of the trajectories of the participants in the pursuit task when moving at a constant speed, while following the predicted trajectories. The predicted trajectories form a one-parameter network of continuous lines of the first order of smoothness. The predicted trajectories are calculated taking into account the restrictions on the curvature of the participant in the pursuit problem. The fact of restrictions on curvature can be interpreted as restrictions on the angular frequency of rotation of the object of the pursuit problem. Also, the novelty is the calculation of the iterative process of group pursuit of multiple targets, when targets are hit simultaneously or at specified intervals. The calculation of the parameters of the network of predicted trajectories is carried out with a curvature variation in order to achieve the desired temporal effect. The work also simulates the adaptive behavior of the pursuer and the target. The principle of behavior can be expressed on the example of a pursuer with a simple phrase: "You go to the left - I go to the left." This happens at each iteration step in terms of choosing the direction of rotation. For the purpose, the principle of adaptive behavior is expressed by the phrase: "You go to the left - I go to the right." The studies, algorithms and models presented in the monograph can be in demand in the design of autonomously controlled unmanned aerial vehicles with elements of artificial intelligence. The task models in the monograph are supplemented with many animated images, where you can see the research process. Also, the tasks have an implementation in a computer mathematics system and can be transferred to virtual reality systems if necessary.
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Book chapters on the topic "Unmanned ground vehicles"

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Hebert, Martial, Charles E. Thorpe, and Anthony Stentz. "Introduction." In Intelligent Unmanned Ground Vehicles, 1–17. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_1.

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Kay, Jennifer, and Charles E. Thorpe. "STRIPE: Low-Bandwidth and High-Latency Teleoperation." In Intelligent Unmanned Ground Vehicles, 187–202. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_10.

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Stentz, Anthony. "Optimal and Efficient Path Planning for Partially Known Environments." In Intelligent Unmanned Ground Vehicles, 203–20. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_11.

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Brumitt, Barry, and Anthony Stentz. "Dynamic Mission Planning for Multiple Mobile Robots." In Intelligent Unmanned Ground Vehicles, 221–34. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_12.

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Thorpe, Charles E., Omead Amidi, Jay Gowdy, Martial Hebert, and Dean Pomerleau. "Integrating Position Estimation and Perception for Navigation." In Intelligent Unmanned Ground Vehicles, 235–57. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_13.

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Langer, Dirk, Julio K. Rosenblatt, and Martial Hebert. "An Integrated System for Autonomous Off-Road Navigation." In Intelligent Unmanned Ground Vehicles, 259–75. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_14.

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Stentz, Anthony, and Martial Hebert. "A Navigation System for Goal Acquisition in Unknown Environments." In Intelligent Unmanned Ground Vehicles, 277–306. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_15.

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Rosenblatt, Julio K., and Charles E. Thorpe. "A Behavior-based Architecture for Mobile Navigation." In Intelligent Unmanned Ground Vehicles, 19–32. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_2.

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Gowdy, Jay. "SAUSAGES: Between Planning and Action." In Intelligent Unmanned Ground Vehicles, 33–52. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_3.

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Pomerleau, Dean. "Neural Network Vision for Robot Driving." In Intelligent Unmanned Ground Vehicles, 53–72. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6325-9_4.

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Conference papers on the topic "Unmanned ground vehicles"

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Jaczkowski, J., G. Hudas, J. Overholt, E. Hall, M. Ghaffari, J. Lane, B. Brendle, and C. Ka. "Intelligent unmanned ground vehicles." In Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2004. http://dx.doi.org/10.1109/itsc.2004.1399033.

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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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Larsen, Karin R., and Keith Olson. "Intersection navigation for unmanned ground vehicles." In Aerospace/Defense Sensing and Controls, edited by Scott A. Speigle. SPIE, 1996. http://dx.doi.org/10.1117/12.241077.

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Gerhart, Grant R., and Gary Witus. "Sensor deployment on unmanned ground vehicles." In Optics/Photonics in Security and Defence, edited by Edward M. Carapezza. SPIE, 2007. http://dx.doi.org/10.1117/12.736109.

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Haas, Gary. "Test facility for unmanned ground vehicles." In Aerospace/Defense Sensing and Controls, edited by Grant R. Gerhart and Ben A. Abbott. SPIE, 1998. http://dx.doi.org/10.1117/12.317553.

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Qian, Ying, Feitong Wang, Cheng Lin, Shengye Huang, and Xuejia Guo. "Development of military unmanned ground vehicles." In Conference on Optical Sensing and Imaging Technology, edited by Yadong Jiang, Qunbo Lv, Bin Xue, Dengwei Zhang, and Dong Liu. SPIE, 2021. http://dx.doi.org/10.1117/12.2601876.

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Wang, Yunpeng, Long Cheng, Zeng-Guang Hou, Min Tan, and Hongnian Yu. "Coordinated transportation of a group of unmanned ground vehicles." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7260750.

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Oliveira, Tiago, and Pedro Encarnação. "Ground Target Tracking for Unmanned Aerial Vehicles." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-8082.

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Hazra, Bani, Alok Mukherjee, and Vishal Veer Singh. "A Communication Protocol for Unmanned Ground Vehicles." In Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2506095.2506143.

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Murphy, Karl N., and Steven Legowik. "GPS-aided retrotraverse for unmanned ground vehicles." In Aerospace/Defense Sensing and Controls, edited by Scott A. Speigle. SPIE, 1996. http://dx.doi.org/10.1117/12.241076.

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Reports on the topic "Unmanned ground vehicles"

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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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Sellers, D. P., A. J. Ramsbotham, Hal Bertrand, and Nicholas Karvonides. International Assessment of Unmanned Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada534965.

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Richmond, Paul W., George L. Mason, Barry A. Coutermarsh, Jason Pusey, and Victoria D. Moore. Mobility Performance Algorithms for Small Unmanned Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada500849.

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Rogers, Paul D. Army Support to Future Combat Systems Unmanned Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada466892.

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Turnage, Doris. Localization and mapping of unknown locations with unmanned ground vehicles. Engineer Research and Development Center (U.S.), March 2019. http://dx.doi.org/10.21079/11681/32277.

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Iagnemma, Karl. Design and Control of Omnidirectional Unmanned Ground Vehicles for Rough Terrain. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada580067.

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Jones, Randolph M., Ron Arkin, and Nahid Sidki. Intelligent Terrain Analysis and Tactical Support System (ITATSS) for Unmanned Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada434526.

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Iagnemma, Karl. Navigation and Hazard Avoidance for High-Speed Unmanned Ground Vehicles in Rough Terrain. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada498562.

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Shima, Tal, Pantelis Isaiah, and Yoav Gottlieb. Motion Planning and Task Assignment for Unmanned Aerial Vehicles Cooperating with Unattended Ground Sensors. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada619854.

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Fields, MaryAnne, and Bailey T. Haug. Developing a Chemical Reconnaissance Behavior for Unmanned Ground Vehicles Using the OneSAF Battlefield Simulation Tool. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada415682.

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