Relevant bibliographies by topics / Vehicle Navigation

Contents

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

Academic literature on the topic 'Vehicle Navigation'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Vehicle Navigation"

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Moussa, Mohamed, Shady Zahran, Mostafa Mostafa, Adel Moussa, Naser El-Sheimy, and Mohamed Elhabiby. "Optical and Mass Flow Sensors for Aiding Vehicle Navigation in GNSS Denied Environment." Sensors 20, no. 22 (November 17, 2020): 6567. http://dx.doi.org/10.3390/s20226567.

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Nowadays, autonomous vehicles have achieved a lot of research interest regarding the navigation, the surrounding environmental perception, and control. Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) is one of the significant components of any vehicle navigation system. However, GNSS has limitations in some operating scenarios such as urban regions and indoor environments where the GNSS signal suffers from multipath or outage. On the other hand, INS standalone navigation solution degrades over time due to the INS errors. Therefore, a modern vehicle navigation system depends on integration between different sensors to aid INS for mitigating its drift during GNSS signal outage. However, there are some challenges for the aiding sensors related to their high price, high computational costs, and environmental and weather effects. This paper proposes an integrated aiding navigation system for vehicles in an indoor environment (e.g., underground parking). This proposed system is based on optical flow and multiple mass flow sensors integrations to aid the low-cost INS by providing the navigation extended Kalman filter (EKF) with forward velocity and change of heading updates to enhance the vehicle navigation. The optical flow is computed for frames taken using a consumer portable device (CPD) camera mounted in the upward-looking direction to avoid moving objects in front of the camera and to exploit the typical features of the underground parking or tunnels such as ducts and pipes. On the other hand, the multiple mass flow sensors measurements are modeled to provide forward velocity information. Moreover, a mass flow differential odometry is proposed where the vehicle change of heading is estimated from the multiple mass flow sensors measurements. This integrated aiding system can be used for unmanned aerial vehicles (UAV) and land vehicle navigations. However, the experimental results are implemented for land vehicles through the integration of CPD with mass flow sensors to aid the navigation system.
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Ramesh, R., V. Bala Naga Jyothi, N. Vedachalam, G. A. Ramadass, and M. A. Atmanand. "Development and Performance Validation of a Navigation System for an Underwater Vehicle." Journal of Navigation 69, no. 5 (January 26, 2016): 1097–113. http://dx.doi.org/10.1017/s0373463315001058.

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Underwater position data is a key requirement for the navigation and control of unmanned underwater vehicles. The proposed navigation scheme can be used in any vessel or boat for any shallow water vehicle. This paper presents the position estimation algorithm developed for shallow water Remotely Operated Vehicles (ROVs) using attitude data and Doppler Velocity Log data with the initial position from the Global Positioning System (GPS). The navigational sensors are identified using the in-house developed simulation tool in MATLAB, based on the requirement of a position accuracy of less than 5%. The navigation system is built using the identified sensors, Kalman filter and navigation algorithm, developed in LabVIEW software. The developed system is tested and validated for position estimation, with an emulator consisting of a GPS-aided fibre optic gyro-based inertial navigation system as a reference, and it is found that the developed navigation system has a position error of less than 5%.
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Li, Ningbo, Yanbin Gao, Ye Wang, Zhejun Liu, Lianwu Guan, and Xin Liu. "A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering." Sensors 19, no. 8 (April 18, 2019): 1861. http://dx.doi.org/10.3390/s19081861.

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Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems.
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Karadeniz Kartal, Seda, M. Kemal Leblebicioglu, and Emre Ege. "Experimental test of the acoustic-based navigation and system identification of an unmanned underwater survey vehicle (SAGA)." Transactions of the Institute of Measurement and Control 40, no. 8 (May 2018): 2476–87. http://dx.doi.org/10.1177/0142331218756727.

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In this study, a nonlinear mathematical model for an unmanned underwater survey vehicle (SAGA) is obtained. The structure of the mathematical model of the vehicle comes from a Newton–Euler formulation. The three-dimensional motion is realized by a suitable combination of right, left and vertical thrusters. The navigation problem is solved by a combination of the inertial navigation system and acoustic-based measurements, which are integrated to obtain more accurate vehicle navigation data. In addition, a magnetic compass and a depth sensor are used to support vehicle attitude and depth information. A pool experimental set-up is designed for the navigation system. The performance of the resultant navigation system can be analysed by creating suitable system state, measurement and noise models. The vehicle navigational data are improved with a Kalman filter. The mathematical model of the vehicle includes some unknown parameters, such as added mass and damping coefficients. It is not possible to determine all the parameter values as their effect on the state of the system is usually negligible. However, most of the ‘important’ parameters are obtained from a system identification study of the vehicle by means of the estimated navigational data for coupled motion. The entire study is performed in a Matlab/Simulink environment.
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Karadeniz Kartal, Seda, M. Kemal Leblebicioglu, and Emre Ege. "Experimental test of vision-based navigation and system identification of an unmanned underwater survey vehicle (SAGA) for the yaw motion." Transactions of the Institute of Measurement and Control 41, no. 8 (January 31, 2019): 2160–70. http://dx.doi.org/10.1177/0142331219826524.

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In this study, a nonlinear mathematical model for an unmanned underwater survey vehicle (SAGA) is obtained. The structure of the mathematical model of the vehicle comes from a Newton–Euler formulation. The yaw motion is realized by a suitable combination of right and left thrusters. The navigation problem is solved by using the inertial navigation system and vision-based measurements together. These are integrated to more accurately obtain navigation data for the vehicle. In addition, the magnetic compass is used to support the attitude information of the vehicle. A pool experimental set-up is designed to test the navigation system. Performance of the resultant navigation system can be analysed by creating suitable system state, measurement and noise models. The navigational data for the vehicle has been improved using a Kalman filter. The mathematical model of the vehicle includes some unknown parameters such as added mass and damping coefficients. It is not possible to determine all the parameter values as their effects on the state of the system are usually negligible. On the other hand, most of the ‘important’ parameters are obtained based on a system identification study of the vehicle using this estimated navigational data for coupled motion. This study is performed in a MATLAB/Simulink environment.
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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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Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hongxin Hu. "Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 31, 2019): 494–507. http://dx.doi.org/10.1177/0361198119855606.

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Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.
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Elsheikh, Mohamed, Walid Abdelfatah, Aboelmagd Nourledin, Umar Iqbal, and Michael Korenberg. "Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles." Sensors 19, no. 22 (November 9, 2019): 4896. http://dx.doi.org/10.3390/s19224896.

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The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.
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Miller, Paul A., Jay A. Farrell, Yuanyuan Zhao, and Vladimir Djapic. "Autonomous Underwater Vehicle Navigation." IEEE Journal of Oceanic Engineering 35, no. 3 (July 2010): 663–78. http://dx.doi.org/10.1109/joe.2010.2052691.

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Campbell, N. W., M. R. Pout, M. D. J. Priestly, E. L. Dagless, and B. T. Thomas. "Autonomous road vehicle navigation." Engineering Applications of Artificial Intelligence 7, no. 2 (April 1994): 177–90. http://dx.doi.org/10.1016/0952-1976(94)90022-1.

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Dissertations / Theses on the topic "Vehicle Navigation"

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Dryer, Jay E. (Jay Edward) 1970. "Robust autonomous vehicle navigation." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/91363.

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Kaparias, Ioannis. "Reliable dynamic in-vehicle navigation." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498652.

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Friedman, Andrew D. "NAVIGATION AUTONOMY FOR UNMANNED SURFACE VEHICLE." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192451.

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Meira, Guilherme Tebaldi. "Stereo Vision-based Autonomous Vehicle Navigation." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/344.

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Research efforts on the development of autonomous vehicles date back to the 1920s and recent announcements indicate that those cars are close to becoming commercially available. However, the most successful prototypes that are currently being demonstrated rely on an expensive set of sensors. This study investigates the use of an affordable vision system as a planner for the Robocart, an autonomous golf cart prototype developed by the Wireless Innovation Laboratory at WPI. The proposed approach relies on a stereo vision system composed of a pair of Raspberry Pi computers, each one equipped with a Camera Module. They are connected to a server and their clocks are synchronized using the Precision Time Protocol (PTP). The server uses timestamps to obtain a pair of simultaneously captured images. Images are processed to generate a disparity map using stereo matching and points in this map are reprojected to the 3D world as a point cloud. Then, an occupancy grid is built and used as input for an A* graph search that finds a collision-free path for the robot. Due to the non-holonomic constraints of a car-like robot, a Pure Pursuit algorithm is used as the control method to guide the robot along the computed path. The cameras are also used by a Visual Odometry algorithm that tracks points on a sequence of images to estimate the position and orientation of the vehicle. The algorithms were implemented using the C++ language and the open source library OpenCV. Tests in a controlled environment show promising results and the interfaces between the server and the Robocart have been defined, so that the proposed method can be used on the golf cart as soon as the mechanical systems are fully functional.
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Moore, Christopher, Dylan Crocker, Garret Coffman, and Bryce Nguyen. "Telemetry Network for Ground Vehicle Navigation." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595750.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
This paper describes a short distance telemetry network which measures and relays time, space, and position information among a group of ground vehicles. The goal is to allow a lead vehicle to be under human control, or perhaps controlled using advanced autonomous path planning and navigation tools. The telemetry network will then allow a series of inexpensive, unmanned vehicles to follow the lead vehicle at a safe distance. Ultrasonic and infrared signals will be relayed between the vehicles, to allow the following vehicles to locate their position, and track the lead vehicle.
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Smith, Robert. "Terrain-aided navigation of an underwater vehicle." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244626.

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Hu, Jun. "Short-term congestion prediction for vehicle navigation." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535007.

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Forbes, Nicholas Lloyd. "Behavioural adaptation to in-vehicle navigation systems." Thesis, University of Nottingham, 2009. http://eprints.nottingham.ac.uk/10798/.

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This PhD investigates driver behavioural adaptation to in-vehicle navigation systems (IVNS). Behavioural adaptation is receiving an increasing amount of research attention in traffic psychology, but few studies have directly considered the concept in relation to IVNS. The thesis aims were addressed using a range of quantitative and qualitative methodologies. Using two online surveys, over 1300 drivers (including over 1000 IVNS users) were sampled, to identify a range of positive, neutral and negative aspects of end-user behavioural adaptation to IVNS in terms of both safety and navigational efficiency. The first survey (N=450) aimed at drivers in general, showed that IVNS users believe they commit some common driving errors (e.g. misreading signs when leaving a roundabout) significantly less frequently than ordinary drivers who do not use these systems, but that they also feel they drive without fully attending to the road ahead significantly more frequently. The second survey (N=872) was aimed at IVNS users only, and further explored distracted driving. This survey found that the majority of IVNS users have interacted with their system while driving (e.g. to enter a destination), and that some do so frequently. It also showed that system reliability is a key issue affecting most current IVNS users, revealing that some drivers have followed inaccurate as well as illegal and potentially dangerous, system-generated route guidance information in a range of different contexts. A longitudinal diary study (N=20) then collected rich qualitative data from a sample of worker drivers who regularly used their IVNS in unfamiliar areas. The data collected illustrated the diverse contexts in which drivers experience aspects of behavioural adaptation to IVNS identified in the surveys. Both the IVNS user-survey and diary study also identified key demographic individual difference variables (most notably age and computing skill) that were associated with the extent to which driver’s experienced different manifestations of behavioural adaptation to IVNS. Moreover, other individual difference variables (e.g. complacency potential, system-trust, confidence) were found to be associated with more specific behavioural adaptations. Two simulator studies investigated system interaction while driving. The first (N=24) demonstrated the poor degree of correspondence between drivers’ perceptions of driving performance when entering destinations while driving (relative to normal driving) and objective performance differences between these conditions. The second simulator study (N=24) showed that safety and training based interventions designed to reduce the extent to which drivers use IVNS while driving or to improve their performance if they do had only a modest effect on dependent measures. This thesis represents the first attempt in the literature to bring together research from diverse areas of human factors and traffic psychology to consider behavioural adaptation to in-vehicle navigation systems. By associating a range of these issues with behavioural adaptation to IVNS, it has indirectly increased the scope of several salient, previous research findings. Moreover, by investigating many of these issues in depth, using both quantitative and qualitative methodological approaches, it has set the foundation for future work. Such work should aim to explore many of the issues raised, and develop effective remediating or mitigating intervention strategies for negative behavioural adaptations that could adversely affect driving safety, as well as to encourage and support those which may be considered more positive.
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Peterson, Kevin Robert. "Visual navigation for an autonomous mobile vehicle." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/24105.

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Approved for public release; distribution is unlimited
Image understanding for a mobile robotic vehicle is an important and complex task for ensuring safe navigation and extended autonomous operations. The goal of this work is to implement a working vision-based navigation control mechanism within a known environment onboard the autonomous mobile vehicle Yamabico-II. Although installing a working hardware system was not accomplished, the image processing, model description, pattern matching, and positional correction methods have all been implemented and tested on a graphics workstation. A novel approach for straight-edge feature extraction based upon a least squares fitting of edge-related pixels is presented and implemented for the image processing task. A simple method for determining the camera's location and orientation (pose) follows by matching the vertical extracted edges from an image with the linear features of a two-dimensional view of the modeled environment based upon an estimated pose of the robot. Image processing, construction of the two-dimensional view of the model, and pose determination are conducted sequentially in less than one minute for a 646 x 486 pixel image on a 35 MHz processor. The pose determination results have been tested to be accurate to within a few inches for translational error and within one degree rotational error.
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Visser, Wynand. "Automation and navigation of a terrestrial vehicle." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20263.

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Thesis (MScEng)--Stellenbosch University, 2012
ENGLISH ABSTRACT: This thesis presents the design and implementation of an autonomous navigational system and the automation of a practical demonstrator vehicle. It validates the proposed navigation architecture using simple functional navigational modules on the said vehicle. The proposed navigation architecture is a hierarchical structure, with a mission planner at the top, followed by the route planner, the path planner and a vehicle controller with the vehicle hardware at the base. A vehicle state estimator and mapping module runs in parallel to provide feedback data. The controls of an all terrain vehicle are electrically actuated and equipped with feedback sensors to form a complete drive-by-wire solution. A steering controller and velocity control state machine are designed and implemented on an existing on-board controller that includes a six degrees-of-freedom kinematic state estimator. A lidar scanner detects obstacles. The lidar data is mapped in real time to a local three-dimensional occupancy grid using a Bayesian update process. Each lidar beam is projected within the occupancy grid and the occupancy state of a ected cells is updated. A lidar simulation environment is created to test the mapping module before practical implementation. For planning purposes, the three-dimensional occupancy grid is converted to a two-dimensional drivability map. The path planner is an adapted rapidly exploring random tree (RRT) planner, that assumes Dubins car kinematics for the vehicle. The path planner optimises a cost function based on path length and a risk factor that is derived from the drivability map. A simple mission planner that accepts user-de ned waypoints as objectives is implemented. Practical tests veri ed the potential of the navigational structure implemented in this thesis.
AFRIKAANSE OPSOMMING: In hierdie tesis word die ontwerp en implementering van 'n outonome navigasiestelsel weergegee, asook die outomatisering van 'n praktiese demonstrasievoertuig. Dit regverdig die voorgestelde navigasie-argitektuur op die bogenoemde voertuig deur gebruik te maak van eenvoudige, funksionele navigasie-modules. Die voorgestelde navigasie-argitektuur is 'n hi erargiese struktuur, met die missie-beplanner aan die bo-punt, gevolg deur die roetebeplanner, die padbeplanner en voertuigbeheerder, met die voertuighardeware as basisvlak. 'n Voertuigtoestandsafskatter en karteringsmodule loop in parallel om terugvoer te voorsien. Die kontroles van 'n vierwiel-motor ets is elektries geaktueer en met terugvoersensors toegerus om volledig rekenaarbeheerd te wees. 'n Stuur-beheerder en 'n snelheid-toestandmasjien is ontwerp en ge mplementeer op 'n bestaande aanboordverwerker wat 'n kinematiese toestandsafskatter in ses grade van vryheid insluit. 'n Lidar-skandeerder registreer hindernisse. Die lidar-data word in re ele tyd na 'n lokale drie-dimensionele besettingsrooster geprojekteer deur middel van 'n Bayesiese opdateringsproses. Elke lidar-straal word in die besettingsrooster geprojekteer en die besettingstoestand van betrokke selle word opdateer. 'n Lidar-simulasie-omgewing is geskep om die karteringsmodule te toets voor dit ge mplementeer word. Die drie-dimensionele besettingsrooster word na 'n twee-dimensionele rybaarheidskaart verwerk vir beplanningsdoeleindes. Die padbeplanner is 'n aangepaste spoedig-ontdekkende-lukrake-boom en neem Dubinskar kinematika vir die voertuig aan. Die padbeplanner optimeer 'n koste-funksie, gebaseer op padlengte en 'n risiko-faktor, wat vanaf die rybaarheidskaart verkry word. 'n Eenvoudige missie-beplanner, wat via-punte as doelstellings neem, is ge mplementeer. Praktiese toetsritte veri eer die potensiaal van die navigasiestruktuur, soos hier beskryf.
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Books on the topic "Vehicle Navigation"

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Vehicle location and navigation systems. Boston, Mass: Artech House, 1997.

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Diamadopoulos. Lane support algorithms for autonomous vehicle navigation. Manchester: UMIST, 1996.

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Driankov, Dimiter, and Alessandro Saffiotti, eds. Fuzzy Logic Techniques for Autonomous Vehicle Navigation. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1835-2.

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Peterson, Kevin Robert. Visual navigation for an autonomous mobile vehicle. Monterey, Calif: Naval Postgraduate School, 1992.

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Li, Xuefeng, and Chaobing Li. Navigation and Guidance of Orbital Transfer Vehicle. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6334-3.

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Vehicle Navigation and Information Systems Conference (2nd 1991 Dearborn, Mich.). VNIS'91: Vehicle navigation & information systems : conference proceedings. Warrendale: Society of Automotive Engineers, 1991.

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Schiffbauer, William H. A locomotion emulator for testing mine vehicle navigation. Washington: U.S. Dept. of the Interior, Bureau of Mines, 1991.

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Kayirhan, Alp. Sonar based navigation of an autonomous underwater vehicle. Monterey, Calif: Naval Postgraduate School, 1994.

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Engineers, Society of Automotive, and Vehicular Technology Society, eds. Vehicle Navigation & Information Systems Conference proceedings: VNIS '91. Warrendale, PA: Society of Automotive Engineers, 1991.

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McKeon, James Bernard. Incorporation of GPS/INS small autonomous underwater vehicle navigation. Monterey, Calif: Naval Postgraduate School, 1992.

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Book chapters on the topic "Vehicle Navigation"

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Hofmann-Wellenhof, Bernhard, Klaus Legat, and Manfred Wieser. "Vehicle and traffic management." In Navigation, 337–59. Vienna: Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-6078-7_15.

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Ng, Tian Seng. "Vehicle Navigation Computing." In Robotic Vehicles: Systems and Technology, 43–48. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6687-9_6.

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Raspopov, Vladimir Y., Alexander V. Nebylov, Sukrit Sharan, and Bijay Agarwal. "Unmanned Aerospace Vehicle Navigation." In Aerospace Navigation Systems, 321–60. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119163060.ch10.

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Leonard, John J., and Alexander Bahr. "Autonomous Underwater Vehicle Navigation." In Springer Handbook of Ocean Engineering, 341–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-16649-0_14.

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Dickmanns, Ernst-Dieter. "Vehicle Guidance by Computer Vision." In High Precision Navigation, 86–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74585-0_5.

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Sharp, Ian, and Kegen Yu. "NLOS Mitigation for Vehicle Tracking." In Navigation: Science and Technology, 505–30. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8791-2_16.

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Cheng, Hong. "Vehicle Navigation Using Global Views." In Autonomous Intelligent Vehicles, 109–21. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2280-7_8.

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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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Nilsson, John-Olof, Dave Zachariah, and Isaac Skog. "Global Navigation Satellite Systems: An Enabler for In-Vehicle Navigation." In Handbook of Intelligent Vehicles, 311–42. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-085-4_13.

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Zhang, Xiubin, and Muhammad Mansoor Khan. "Intelligent Vehicle Navigation and Traffic System." In Principles of Intelligent Automobiles, 175–209. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2484-0_5.

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Conference papers on the topic "Vehicle Navigation"

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Avram, Camelia, Adina Astilean, and Dan Radu. "Vehicle Navigation System." In 26th Conference on Modelling and Simulation. ECMS, 2012. http://dx.doi.org/10.7148/2012-0236-0240.

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Sha, Wenjie, Daehan Kwak, Badri Nath, and Liviu Iftode. "Social vehicle navigation." In the 14th Workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2444776.2444798.

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Chen, Cheng-wu, Ke Qiu, and Tsu-shuan Chang. "Land vehicle navigation." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272771.

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Krage, Mark K. "The TravTek Driver Information System." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912820.

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Dingus, Thomas A., Janeth T. Carpenter, Francis E. Szczublewski, Mark K. Krage, Linda G. Means, and Rebecca N. Fleischman. "Human Factors Engineering the TravTek Driver Interface." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912821.

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Rupert, Robert L. "The TravTek Traffic Management Center and Traffic Information Network." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912822.

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Taylor, Kent B. "TravTek - Information and Services Center." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912823.

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Rilett, L. R., M. Van Aerde, G. MacKinnon, and M. Krage. "Simulating the TravTek Route Guidance Logic Using the Integration Traffic Model." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912824.

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Banks, K. M. "Datatrak Automatic Vehicle Location System in Operational Use in the UK." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912825.

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McLellan, James F., Edward J. Krakiwsky, David R. Huff, Ellen L. Kitagawa, and Michael R. Gervais. "Fleet Management Trials in Western Canada." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912826.

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Reports on the topic "Vehicle Navigation"

1

Rosenfeld, Azriel, and Lary S. Davis. Autonomous Vehicle Navigation. Fort Belvoir, VA: Defense Technical Information Center, May 1986. http://dx.doi.org/10.21236/ada170379.

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Miller, P. A., J. Farrell, Y. Zhao, and V. Djapic. Autonomous Underwater Vehicle Navigation. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada485442.

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Riseman, Edward M., and Allen R. Hanson. Dynamic Image Interpretation for Autonomous Vehicle Navigation. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada213172.

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Morrow, J. D. Optimal sensor fusion for land vehicle navigation. Office of Scientific and Technical Information (OSTI), October 1990. http://dx.doi.org/10.2172/6242453.

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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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FOGLER, ROBERT J. Multi- and Hyper-Spectral Sensing for Autonomous Ground Vehicle Navigation. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/820893.

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Kahn, Aaron D., and Daniel J. Edwards. Navigation, Guidance and Control For the CICADA Expendable Micro Air Vehicle. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada623280.

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Braasch, Michael S. Unmanned Aerial Vehicle (UAV) Swarming and Formation Flight Navigation VIA LIDAR/INS. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada456221.

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Hirokawa, Rui, Naoyuki Kajiwara, and Junichi Takiguchi. Carrier-Phase GPS/DR/LS Hybrid Navigation for an Autonomous Ground Vehicle. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0283.

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You might also be interested in the extended bibliographies on the topic 'Vehicle Navigation' for particular source types:
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