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Relevant bibliographies by topics / Autonomous guided vehicles

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Academic literature on the topic 'Autonomous guided vehicles'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Autonomous guided vehicles"

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Lau, Henry Y. K., Vicky W. K. Wong, and Ivan S. K. Lee. "Immunity-based autonomous guided vehicles control." Applied Soft Computing 7, no. 1 (January 2007): 41–57. http://dx.doi.org/10.1016/j.asoc.2005.02.003.

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Vida, Gábor, and Péter Váradi. "Irregular operation of autonomous vehicles." Production Engineering Archives 20, no. 20 (September 1, 2018): 8–11. http://dx.doi.org/10.30657/pea.2018.20.02.

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Abstract Today, with the spread of autonomous functions in vehicles, the role accountability for causing accidents is emphasized. Self-guided functions work in certain traffic situations, but accidents happen, and, therefore, the following article presents an analysis of the issue. Its purpose is to show that vehicles with self-drive functionality do not provide the driver's level of safety that vehicle manufacturers suggest. In this article, four recent events and an analysis whether these accidents could have been avoided a human driver or how they could have happened with appropriate self-drive function. In each of the investigated cases, vehicles equipped with self-drive function are involved. Based on the evaluation and assessment of accidents, conclusions are drawn whether current self-propelled vehicles provide the safety level that drivers and society expect from these vehicles. The reconstruction of the accident process is illustrated with the help of a vehicle simulation program, with the resultant parameters being given a special emphasis, in particular to the avoidance of the accident.

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TSUMURA, Toshihiro. "Positioning Technologies for Autonomous Vehicles. Positioning of Automated Guided Vehicle." Journal of the Japan Society for Precision Engineering 65, no. 10 (1999): 1398–401. http://dx.doi.org/10.2493/jjspe.65.1398.

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Hjalmarsson-Jordanius, Anders, Mikael Edvardsson, Martin Romell, Johan Isacson, Carl-Johan Aldén, and Niklas Sundin. "Autonomous Transport: Transforming Logistics through Driverless Intelligent Transportation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 7 (September 17, 2018): 24–33. http://dx.doi.org/10.1177/0361198118796968.

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How can autonomous technology be used beyond end-customer autonomous driving features? This position paper addresses this problem by exploring a novel autonomous transport solution applied in the automotive logistics domain. We propose that factory-complete cars can be transformed to become their own autonomous guided vehicles and thus transport themselves when being moved from the factory for shipment. Cars equipped with such a system are driverless and use an onboard autonomous transport solution combined with the advanced driver assistance systems pre-installed in the car for end-customer use. The solution uses factory-equipped sensors as well as the connectivity infrastructure installed in the car. This means that the solution does not require any extra components to enable the car to transport itself autonomously to complete a transport mission in the logistics chain. The solution also includes an intelligent off-board traffic control system that defines the transport mission and manages the interaction between vehicles during systems operation. A prototype of the system has been developed which was tested successfully in live trials at the Volvo Car Group plant in Gothenburg Sweden in 2017. In the paper, autonomous transport is positioned in between autonomous guided vehicles and autonomous driving technology. A review of the literature on autonomous vehicle technology offers contextual background to this positioning. The paper also presents the solution and displays lessons learned from the live trials. Finally, other use areas are introduced for driverless autonomous transport beyond the automotive logistics domain that is the focus of this paper.

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Curiel-Ramirez, Luis A., Ricardo A. Ramirez-Mendoza, Rolando Bautista-Montesano, M. Rogelio Bustamante-Bello, Hugo G. Gonzalez-Hernandez, Jorge A. Reyes-Avedaño, and Edgar Cortes Gallardo-Medina. "End-to-End Automated Guided Modular Vehicle." Applied Sciences 10, no. 12 (June 26, 2020): 4400. http://dx.doi.org/10.3390/app10124400.

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Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution.

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Scholz, Michael, Xu Zhang, and Jörg Franke. "Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization." Applied Mechanics and Materials 882 (July 2018): 90–95. http://dx.doi.org/10.4028/www.scientific.net/amm.882.90.

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The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.

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Xidias, Elias K., and Philip N. Azariadis. "Mission design for a group of autonomous guided vehicles." Robotics and Autonomous Systems 59, no. 1 (January 2011): 34–43. http://dx.doi.org/10.1016/j.robot.2010.10.003.

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Mueller, H., H. Neubauer, and D. A. Mlynski. "Path Assignment to a Set of Autonomous Guided Vehicles." IFAC Proceedings Volumes 21, no. 16 (October 1988): 267–70. http://dx.doi.org/10.1016/s1474-6670(17)54621-2.

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Berman, S., Y. Edan, and Mo Jamshidi. "Navigation of decentralized autonomous automatic guided vehicles in material handling." IEEE Transactions on Robotics and Automation 19, no. 4 (August 2003): 743–49. http://dx.doi.org/10.1109/tra.2003.814513.

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Sylvester Tlale, Nkgatho. "On distributed mechatronics controller for omni‐directional autonomous guided vehicles." Industrial Robot: An International Journal 33, no. 4 (July 2006): 278–84. http://dx.doi.org/10.1108/01439910610667890.

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Dissertations / Theses on the topic "Autonomous guided vehicles"

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Hague, Tony. "Motion planning for autonomous guided vehicles." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358592.

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Anderson, Jonathan D. "Semi Autonomous Vehicle Intelligence: Real Time Target Tracking For Vision Guided Autonomous Vehicles." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1750.pdf.

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Wang, Tingkai. "Navigation and control of autonomous guided vehicles." Thesis, University of Wolverhampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264041.

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Chikosi, Gerald. "Autonomous guided vehicle for agricultural application." Thesis, Nelson Mandela Metropolitan University, 2014. http://hdl.handle.net/10948/d1016164.

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With the world's population expected to reach nine billion by 2050, agricultural production will have to double to meet this growing demand. Hence, a need for better infrastructure to enhance farming efficiency becomes apparent. There are a number of solutions that have been developed to date that are commercially available. They range from genetically modified seeds and bio/green fertilizers to advanced farming machinery amongst others. However most of the farming equipment developed has drawbacks such as: heavy weight – this leads to reduced yields due to soil compacting; human dependency – constant monitoring and controlling is needed; light dependency – excludes usage during the night or when visibility is poor. Therefore, a possible solution will be researched to enhance the evolution of farming equipment. Furthermore, a model will be developed for testing and verifying the research.

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Griffiths, Ian. "Microcontroller implementation of artificial intelligence for autonomous guided vehicles." Thesis, University of Wolverhampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266837.

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UjvaÌri, SaÌndor. "Simulation in automated guided vehicle system design." Thesis, De Montfort University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275545.

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Pears, Nicholas Edwin. "The low-level guidance of an experimental autonomous vehicle." Thesis, Durham University, 1989. http://etheses.dur.ac.uk/6731/.

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This thesis describes the data processing and the control that constitutes a method of guidance for an autonomous guided vehicle (AGV) operating in a predefined and structured environment such as a warehouse or factory. A simple battery driven vehicle has been constructed which houses an MC68000 based microcomputer and a number of electronic interface cards. In order to provide a user interface, and in order to integrate the various aspects of the proposed guidance method, a modular software package has been developed. This, along with the research vehicle, has been used to support an experimental approach to the research. The vehicle's guidance method requires a series of concatenated curved and straight imaginary Unes to be passed to the vehicle as a representation of a planned path within its environment. Global position specifications for each line and the associated AGV direction and demand speed for each fine constitute commands which are queued and executed in sequence. In order to execute commands, the AGV is equipped with low level sensors (ultrasonic transducers and optical shaft encoders) which allow it to estimate and correct its global position continually. In addition to a queue of commands, the AGV also has a pre-programmed knowledge of the position of a number of correction boards within its environment. These are simply wooden boards approximately 25cm high and between 2 and 5 metres long with small protrusions ("notches") 4cm deep and 10cm long at regular (Im) intervals along its length. When the AGV passes such a correction board, it can measure its perpendicular distance and orientation relative to that board using two sets of its ultrasonic sensors, one set at the rear of the vehicle near to the drive wheels and one set at the front of the vehicle. Data collected as the vehicle moves parallel to a correction board is digitally filtered and subsequently a least squares line fitting procedure is adopted. As well as improving the reliability and accuracy of orientation and distance measurements relative to the board, this provides the basis for an algorithm with which to detect and measure the position of the protrusions on the correction board. Since measurements in three planar, local coordinates can be made (these are: x, the distance travelled parallel to a correction board; and y,the perpendicular distance relative to a correction board; and Ɵ, the clockwise planar orientation relative to the correction board), global position estimation can be corrected. When position corrections are made, it can be seen that they appear as step disturbances to the control system. This control system has been designed to allow the vehicle to move back onto its imaginary line after a position correction in a critically damped fashion and, in the steady state, to track both linear and curved command segments with minimum error.

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Brown, Alan C. "Logic and real-time systems." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282413.

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Robinson, Stephen David. "The design and intelligent control of an autonomous mobile robot." Thesis, Durham University, 1996. http://etheses.dur.ac.uk/5341/.

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This thesis presents an investigation into the problems of exploration, map building and collision free navigation for intelligent autonomous mobile robots. The project began with an extensive review of currently available literature in the field of mobile robot research, which included intelligent control techniques and their application. It became clear that there was scope for further development with regard to map building and exploration in new and unstructured environments. Animals have an innate propensity to exhibit such abilities, and so the analogous use of artificial neural networks instead of actual neural systems was examined for use as a method of robot mapping. A simulated behaviour based mobile robot was used in conjunction with a growing cell structure neural network to map out new environments. When using the direct application of this algorithm, topological irregularities were observed to be the direct result of correlations within the input data stream. A modification to this basic system was shown to correct the problem, but further developments would be required to produce a generic solution. The mapping algorithms gained through this approach, although more similar to biological systems, are computationally inefficient in comparison to the methods which were subsequently developed. A novel mapping method was proposed based on the robot creating new location vectors, or nodes, when it exceeded a distance threshold from its mapped area. Network parameters were developed to monitor the state of growth of the network and aid the robot search process. In simulation, the combination of the novel mapping and search process were shown to be able to construct maps which could be subsequently used for collision free navigation. To develop greater insights into the control problem and to validate the simulation work the control structures were ported to a prototype mobile robot. The mobile robot was of circular construction, with a synchro-drive wheel configuration, and was equipped with eight ultrasonic distance sensors and an odometric positioning system. It was self-sufficient, incorporating all its power and computational resources. The experiments observed the effects of odometric drift and demonstrated methods of re-correction which were shown to be effective. Both the novel mapping method, and a new algorithm based on an exhaustive mesh search, were shown to be able to explore different environments and subsequently achieve collision free navigation. This was shown in all cases by monitoring the estimates in the positional error which remained within fixed bounds.

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Cawood, Gareth James. "Design of a low-cost autonomous guided cart for material handling." Thesis, Nelson Mandela Metropolitan University, 2015.

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This dissertation covers the design and manufacture of an autonomous guided cart (AGC) for use in the material handling industry. General Motors South Africa (GMSA) requires a low-cost AGC for use in their Struandale plant. A budget of R35 000 per unit was proposed. The researcher, in collaboration with staff at GM, compiled a list of engineering requirements for the AGC. After research into the unique problems of the project, an examination of a previous design attempt by staff of GM Thailand, the researcher developed a new design, the subject of this report. Different solutions for each design problem were investigated before the design was finalised. A three-wheeled vehicle was designed making use of two motors in a differential-drive setup to control motion. Navigation is via a line-following mechanism, using an induction sensor-array in conjunction with a pre-laid metallic strip. To aid the design, the system was modelled to understand the different control elements at play. The researcher developed software for several aspects of the design: for the PLC controlling the system and motors; for a microcontroller that communicates with the PLC and a wireless module; for a computer server that communicates with a second wireless device, receiving information from the PLC; and a web interface to view this information. These form the SCADA integration of the project. The final product meets the GMSA specifications. It is a robot capable of towing a trolley of mass not exceeding 350 kg. While the robot is able to navigate a pre-laid route, it cannot reliably stop at marked locations. It is possible to monitor the system via a web-interface. The robot is capable of operating for an entire 8-hour shift before the batteries need to be recharged. The total cost of the prototype was R26 340.

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Books on the topic "Autonomous guided vehicles"

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Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. Autonomous Guided Vehicles. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14747-5.

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Wang, Tingkai. Navigation and control of autonomous guided vehicles. Wolverhampton: University of Wolverhampton, 1998.

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Griffiths, Ian J. Microcontroller implementation of artificial intelligence for autonomous guided vehicles. Wolverhampton: University of Wolverhampton, 1998.

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Nordman, Douglas B. A computer simulation study of mission planning and control for the NPS autonomous underwater vehicle. Monterey, Calif: Naval Postgraduate School, 1989.

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author, Kalra Nidhi, Stanley Karlyn D. author, Sorensen Paul 1967 author, Samaras Constantine author, Oluwatola Oluwatobi A. author, Rand Corporation, and Rand Transportation, Space, and Technology (Program), eds. Autonomous vehicle technology: A guide for policymakers. Santa Monica, CA: Rand Corporation, 2014.

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Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. Autonomous Guided Vehicles: Methods and Models for Optimal Path Planning. Springer, 2015.

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Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. Autonomous Guided Vehicles: Methods and Models for Optimal Path Planning. Springer, 2016.

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Leonard, C. D. Autonomous Vehicles: Your Ultimate Guide to the Past, Present and Future of Autonomous Vehicles. Len's eBooks, 2018.

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Autonomous Vehicle Technology: A Guide for Policymakers. RAND Corporation, 2014. http://dx.doi.org/10.7249/rr443.

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Driver Reactions to Automated Vehicles: A Practical Guide for Design and Evaluation. Taylor & Francis Group, 2018.

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Book chapters on the topic "Autonomous guided vehicles"

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Durrant-Whyte, Hugh, David Rye, and Eduardo Nebot. "Localization of Autonomous Guided Vehicles." In Robotics Research, 613–25. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1021-7_69.

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Merklinger, A. "Performance Data of Dead Reckoning Procedures for Non Guided Vehicles." In Information Processing in Autonomous Mobile Robots, 121–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-07896-9_9.

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Besant, C. B. "Guidance and navigation techniques for guided and autonomous vehicles." In Material Flow Systems in Manufacturing, 368–87. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2498-4_13.

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Santos Filho, D. J., and P. E. Miyagi. "Enhanced Mark Flow Graph to Control Autonomous Guided Vehicles." In Computer Applications in Production Engineering, 856–65. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34879-7_89.

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Bostelman, Roger, and Elena Messina. "Towards Development of an Automated Guided Vehicle Intelligence Level Performance Standard." In Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor, 1–22. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2016. http://dx.doi.org/10.1520/stp159420150054.

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Song, Mengxuan, Timothy Gordon, Yinqi Liu, and Jun Wang. "Flow Field and Neural Network Guided Steering Control for Rigid Autonomous Vehicles." In Lecture Notes in Mechanical Engineering, 1129–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38077-9_132.

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Bähr, Maik, Reik V. Donner, and Thomas Seidel. "A Pedestrian Dynamics Based Approach to Autonomous Movement Control of Automatic Guided Vehicles." In Lecture Notes in Production Engineering, 175–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30749-2_13.

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Cupek, Rafal, Marek Drewniak, Marcin Fojcik, Erik Kyrkjebø, Jerry Chun-Wei Lin, Dariusz Mrozek, Knut Øvsthus, and Adam Ziebinski. "Autonomous Guided Vehicles for Smart Industries – The State-of-the-Art and Research Challenges." In Lecture Notes in Computer Science, 330–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50426-7_25.

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Banta, Larry E. "Guided Vehicle Technology: From Automated to Autonomous." In Progress in Materials Handling and Logistics, 3–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-662-09512-6_1.

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Cameron, Stephen. "A Geometric Database for the Oxford Autonomous Guided Vehicle." In CAD Based Programming for Sensory Robots, 511–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83625-1_24.

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Conference papers on the topic "Autonomous guided vehicles"

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de Alkmin e Silva, Ludmila Corrêa, Geraldo G. Delgado Neto, Franco Giuseppe Dedini, Carlos Cesar Ap Eguti, Martin Ernst, and Rainer Nordmann. "Strategies of Control Applied in Autonomous Guided Vehicles." In 2008 SAE Brasil Congress and Exhibit. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2008. http://dx.doi.org/10.4271/2008-36-0363.

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Lu, Jian, Kyoko Hamajima, and Koji Ishihara. "Hybrid machine vision method for autonomous guided vehicles." In Electronic Imaging 2003, edited by Martin A. Hunt and Jeffery R. Price. SPIE, 2003. http://dx.doi.org/10.1117/12.474027.

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Holvoet, T. "Decentralized control of autonomous guided vehicles for scalable warehouse systems." In IEE Seminar on Autonomous Agents in Control. IEE, 2005. http://dx.doi.org/10.1049/ic:20050178.

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Ye, Aixue, Haibing Zhu, Zhengdong Xu, Chenxi Sun, and Kui Yuan. "A vision-based guidance method for autonomous guided vehicles." In 2012 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2012. http://dx.doi.org/10.1109/icma.2012.6285133.

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Varga, Robert, and Sergiu Nedevschi. "Vision-based autonomous load handling for automated guided vehicles." In 2014 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2014. http://dx.doi.org/10.1109/iccp.2014.6937003.

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Catal, Ozan, Sam Leroux, Cedric De Boom, Tim Verbelen, and Bart Dhoedt. "Anomaly Detection for Autonomous Guided Vehicles using Bayesian Surprise." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341386.

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Ducrot, Andre, Yann Dumortier, Isabelle Herlin, and Vincent Ducrot. "Real-time quasi dense two-frames depth map for Autonomous Guided Vehicles." In 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2011. http://dx.doi.org/10.1109/ivs.2011.5940507.

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Dumortier, Yann, Isabelle Herlin, and Andre Ducrot. "4-D Tensor Voting motion segmentation for obstacle detection in autonomous guided vehicle." In 2008 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2008. http://dx.doi.org/10.1109/ivs.2008.4621203.

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Plosz, Sandor, and Pal Varga. "Security and safety risk analysis of vision guided autonomous vehicles." In 2018 IEEE Industrial Cyber-Physical Systems (ICPS). IEEE, 2018. http://dx.doi.org/10.1109/icphys.2018.8387658.

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Chen, Chao, Markus Rickert, and Alois Knoll. "Path planning with orientation-aware space exploration guided heuristic search for autonomous parking and maneuvering." In 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225838.

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