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Journal articles on the topic 'Automated guided vehicle systems – Evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Lee, Jim, Richard Hoo-Gon Choi, and Majid Khaksar. "Evaluation of automated guided vehicle systems by simulation." Computers & Industrial Engineering 19, no. 1-4 (January 1990): 318–21. http://dx.doi.org/10.1016/0360-8352(90)90130-e.

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2

Berman, Sigal, Edna Schechtman, and Yael Edan. "Evaluation of automatic guided vehicle systems." Robotics and Computer-Integrated Manufacturing 25, no. 3 (June 2009): 522–28. http://dx.doi.org/10.1016/j.rcim.2008.02.009.

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3

Do Couto, Dylan, Joseph Butterfield, Adrian Murphy, and Joseph Coleman. "Methods of Evaluating 3D Perception Systems for Unstructured Autonomous Logistics." Journal of Computational Vision and Imaging Systems 6, no. 1 (January 15, 2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3558.

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This study introduces methods of evaluating 3D perception systems, such as Time of Flight (ToF) systems, for automated logistics applications in unstructured environments. Here perception is defined as a system’s understanding of its environment and the Objects Of Interest (OOI) within that environment, through hardware consisting of cameras or depth sensors. Current computer guided machinery that rely on perception systems, such as an Autonomous Guided Vehicle (AGV), require structured environments that are specifically designed for such a machine. Unstructured environments include warehouses or manufacturing facilities that have not been tailor designed or structured specifically for the purpose of using a computer guided machine. In this study, two methods are proposed to assess 3D systems proposed for autonomous logistics in unstructured environments. The results of this study indicate that the methods presented here are suitable for future and comparative 3D perception and evaluation in this space.

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4

RAJU, K. RAVI, and O. V. KRISHNAIAH CHETTY. "Design and evaluation of automated guided vehicle systems for flexible manufacturing systems: an extended timed Petri net-based approach." International Journal of Production Research 31, no. 5 (May 1993): 1069–96. http://dx.doi.org/10.1080/00207549308956776.

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5

Zheng, Kun, Dunbing Tang, Adriana Giret, Miguel A. Salido, and Zelei Sang. "A hormone regulation–based approach for distributed and on-line scheduling of machines and automated guided vehicles." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 1 (August 5, 2016): 99–113. http://dx.doi.org/10.1177/0954405416662078.

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With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation–based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.

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6

Nai Chieh, Wei, and Lin Hsiao Kang. "Evaluation of automated guided vehicle systems in thin film transistor liquid crystal display (TFT-LCD) Array manufacturing process." Scientific Research and Essays 7, no. 41 (October 27, 2012): 3542–48. http://dx.doi.org/10.5897/sre11.1991.

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7

Wolter, Stefan, Giancarlo Caccia Dominioni, Sebastian Hergeth, Fabio Tango, Stuart Whitehouse, and Frederik Naujoks. "Human–Vehicle Integration in the Code of Practice for Automated Driving." Information 11, no. 6 (May 27, 2020): 284. http://dx.doi.org/10.3390/info11060284.

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The advancement of SAE Level 3 automated driving systems requires best practices to guide the development process. In the past, the Code of Practice for the Design and Evaluation of ADAS served this role for SAE Level 1 and 2 systems. The challenges of Level 3 automation make it necessary to create a new Code of Practice for automated driving (CoP-AD) as part of the public-funded European project L3Pilot. It provides the developer with a comprehensive guideline on how to design and test automated driving functions, with a focus on highway driving and parking. A variety of areas such as Functional Safety, Cybersecurity, Ethics, and finally the Human–Vehicle Integration are part of it. This paper focuses on the latter, the Human Factors aspects addressed in the CoP-AD. The process of gathering the topics for this category is outlined in the body of the paper. Thorough literature reviews and workshops were part of it. A summary is given on the draft content of the CoP-AD Human–Vehicle Integration topics. This includes general Human Factors related guidelines as well as Mode Awareness, Trust, and Misuse. Driver Monitoring is highlighted as well, together with the topic of Controllability and the execution of Customer Clinics. Furthermore, the Training and Variability of Users is included. Finally, the application of the CoP-AD in the development process for Human-Vehicle Integration is illustrated.

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8

Foit, Krzysztof, Grzegorz Gołda, and Adrian Kampa. "Integration and Evaluation of Intra-Logistics Processes in Flexible Production Systems Based on OEE Metrics, with the Use of Computer Modelling and Simulation of AGVs." Processes 8, no. 12 (December 14, 2020): 1648. http://dx.doi.org/10.3390/pr8121648.

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The article presents the problems connected with the performance evaluation of a flexible production system in the context of designing and integrating production and logistics subsystems. The goal of the performed analysis was to determine the parameters that have the most significant influence on the productivity of the whole system. The possibilities of using automated machine tools, automatic transport vehicles, as well as automated storage systems were pointed out. Moreover, the exemplary models are described, and the framework of simulation research related to the conceptual design of new production systems are indicated. In order to evaluate the system’s productivity, the use of Overall Equipment Efficiency (OEE) metrics was proposed, which is typically used for stationary resources such as machines. This paper aims to prove the hypothesis that the OEE metric can also be used for transport facilities such as Automated Guided Vehicles (AGVs). The developed models include the parameters regarding availability and failure of AGVs as well as production efficiency and quality, which allows the more accurate mapping of manufacturing processes. As the result, the Overall Factory Efficiency (OFE) and Overall Transport Efficiency (OTE) metrics were obtained. The obtained outcomes can be directly related to similar production systems that belong to World Class Manufacturing (WCM) or World Class Logistics (WCL), leading to the in-depth planning of such systems and their further improvement in the context of the Industry 4.0.

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9

Fazlollahtabar, Hamed, and Seyed Taghi Akhavan Niaki. "Integration of fault tree analysis, reliability block diagram and hazard decision tree for industrial robot reliability evaluation." Industrial Robot: An International Journal 44, no. 6 (October 16, 2017): 754–64. http://dx.doi.org/10.1108/ir-06-2017-0103.

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Purpose This paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability. Design/methodology/approach For practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose. Findings Hazard decision tree (HDT) was configured to compute the hazards of each component and the whole AGV robot system. Through this research, a promising technical approach was established, allowing decision-makers to identify the critical components of AGVs along with their crucial hazard phases at the design stage. Originality/value As complex systems have become global and essential in today’s society, their reliable design and determination of their availability have turned into very important tasks for managers and engineers. Industrial robots are examples of these complex systems that are being increasingly used for intelligent transportation, production and distribution of materials in warehouses and automated production lines.

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10

Nantogma, Sulemana, Keyu Pan, Weilong Song, Renwei Luo, and Yang Xu. "Towards Realizing Intelligent Coordinated Controllers for Multi-USV Systems Using Abstract Training Environments." Journal of Marine Science and Engineering 9, no. 6 (May 22, 2021): 560. http://dx.doi.org/10.3390/jmse9060560.

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Unmanned autonomous vehicles for various civilian and military applications have become a particularly interesting research area. Despite their many potential applications, a related technological challenge is realizing realistic coordinated autonomous control and decision making in complex and multi-agent environments. Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. However, the complexity of the physical environment, unrealistic assumptions, and lack of abstract physical environments derail the process of transition from simulation to real systems. This work presents a modular framework for automated data acquisition, training, and the evaluation of multiple unmanned surface vehicles controllers that facilitate prior knowledge integration and human-guided learning in a closed-loop. To realize this, we first present a digital maritime environment of multiple unmanned surface vehicles that abstracts the real-world dynamics in our application domain. Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents’ behaviors and action selection in a multi-agent environment is presented. Evaluation scenarios of different combat missions are presented to demonstrate the performance of the system. Simulation results show that the resulting controllers can achieved an average wining rate between 52% and 98% in all test cases, indicating the effectiveness of the proposed approach and its feasibility in realizing adaptive controllers for efficient multiple unmanned systems’ cooperative decision making. We believe that this system can facilitate the simulation, data acquisition, training, and evaluation of practical cooperative unmanned vehicles’ controllers in a closed-loop.

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11

Zhang, Fuqiang, and Jingjing Li. "An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems." Journal of Advanced Manufacturing Systems 17, no. 03 (July 30, 2018): 375–90. http://dx.doi.org/10.1142/s0219686718500221.

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To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

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12

Cottrell, Wayne D., and Dharminder Pal. "Evaluation of Pedestrian Data Needs and Collection Efforts." Transportation Research Record: Journal of the Transportation Research Board 1828, no. 1 (January 2003): 12–19. http://dx.doi.org/10.3141/1828-02.

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Research was done to evaluate the extent to which the pedestrian data collection efforts of transportation agencies in the United States are addressing pedestrian safety factors. There was also consideration of how pedestrian data collection can be improved to facilitate the monitoring of these factors. Fifteen pedestrian safety issues are identified based on a literature review and examination of pedestrian–vehicle crashes in Utah. A 2001 survey of U.S. transportation agencies indicated that 45 (75%) of the 60 respondents were counting pedestrians at various locations. Hand counting, the recording of push-button use, and video cameras were methods used to collect data. Automated systems, such as position sensors and image processing, were not used to count pedestrians. The use of advanced data collection technologies is not critical to the resolution of pedestrian safety concerns, although permanent counting installations might increase data collection efficiency. Only 4 of the 15 pedestrian safety issues were specifically being addressed by the agencies’ data collection efforts. Their existing methods could, however, be used to target seven additional safety factors. The development of a pedestrian data monitoring guide is recommended; an outline is proposed. Several agencies admitted that pedestrian volumes did not affect their pedestrian treatments. Evidently, some transportation agencies could benefit from direction on how to relate pedestrian demand and behavior data to safety improvements.

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13

Xie, Zhang, He, You, Fan, Yu, and Li. "Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System." Sensors 19, no. 16 (August 13, 2019): 3540. http://dx.doi.org/10.3390/s19163540.

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Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS.

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14

Kasilingam, R. G., and S. L. Gobal. "Vehicle requirements model for automated guided vehicle systems." International Journal of Advanced Manufacturing Technology 12, no. 4 (July 1996): 276–79. http://dx.doi.org/10.1007/bf01239614.

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15

Interrante, Leslie D., and Daniel M. Rochowiak. "Active rescheduling for automated guided vehicle systems." Intelligent Systems Engineering 3, no. 2 (1994): 87. http://dx.doi.org/10.1049/ise.1994.0012.

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16

LIM, Jae Kook, Kap Hwan KIM, Ki Young KIM, Teruo TAKAHASHI, and Kazuho YOSHIMOTO. "Dynamic Routing in Automated Guided Vehicle Systems." JSME International Journal Series C 45, no. 1 (2002): 323–32. http://dx.doi.org/10.1299/jsmec.45.323.

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17

Hsieh, Ling‐Feng, and D. Y. Sha. "A design process for tandem automated guided vehicle systems: the concurrent design of machine layout and guided vehicle routes in tandem automated guided vehicle systems." Integrated Manufacturing Systems 7, no. 6 (December 1996): 30–38. http://dx.doi.org/10.1108/09576069610151167.

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18

Jakubiec, Beata. "Power supply systems of Automated Guided Vehicle l." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 6 (June 30, 2018): 486–89. http://dx.doi.org/10.24136/atest.2018.118.

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The development of technologies in the field of energy storage and loader solutions has influenced the creation of many ways to power the automatic trucks. The article discusses the technologies of power systems used in Automated Guided Vehicle.

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19

SEZEN, Bülent. "Modeling Automated Guided Vehicle Systems in Material Handling." Doğuş Üniversitesi Dergisi 2, no. 4 (April 27, 2003): 207–16. http://dx.doi.org/10.31671/dogus.2019.319.

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20

SEO, Y., and P. J. EGBELU. "Flexible guidepath design for automated guided vehicle systems." International Journal of Production Research 33, no. 4 (April 1995): 1135–56. http://dx.doi.org/10.1080/00207549508930197.

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21

GASKINS, R. J., and J. M. A. TANCHOCO. "Flow path design for automated guided vehicle systems." International Journal of Production Research 25, no. 5 (May 1987): 667–76. http://dx.doi.org/10.1080/00207548708919869.

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22

Goetschalckx, Marc, and Kathleen Henning. "Computer aided engineering of automated guided vehicle systems." Computers & Industrial Engineering 13, no. 1-4 (January 1987): 149–52. http://dx.doi.org/10.1016/0360-8352(87)90070-2.

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23

Salehipour, Amir, Hamed Kazemipoor, and Leila Moslemi Naeini. "Locating workstations in tandem automated guided vehicle systems." International Journal of Advanced Manufacturing Technology 52, no. 1-4 (May 22, 2010): 321–28. http://dx.doi.org/10.1007/s00170-010-2727-y.

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24

De Ryck, M., M. Versteyhe, and K. Shariatmadar. "Resource management in decentralized industrial Automated Guided Vehicle systems." Journal of Manufacturing Systems 54 (January 2020): 204–14. http://dx.doi.org/10.1016/j.jmsy.2019.11.003.

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25

Hark Hwang and Sang Hwi Kim. "Development of dispatching rules for automated guided vehicle systems." Journal of Manufacturing Systems 17, no. 2 (January 1998): 137–43. http://dx.doi.org/10.1016/s0278-6125(98)80026-5.

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26

Ross, Edward A., Farzad Mahmoodi, and Charles T. Mosier. "Tandem Configuration Automated Guided Vehicle Systems: A Comparative Study." Decision Sciences 27, no. 1 (March 1996): 81–102. http://dx.doi.org/10.1111/j.1540-5915.1996.tb00844.x.

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27

Schulze, Lothar, and Lindu Zhao. "Worldwide development and application of automated guided vehicle systems." International Journal of Services Operations and Informatics 2, no. 2 (2007): 164. http://dx.doi.org/10.1504/ijsoi.2007.014518.

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28

Wu, N., and M. Zhou. "Modeling and Deadlock Control of Automated Guided Vehicle Systems." IEEE/ASME Transactions on Mechatronics 9, no. 1 (March 2004): 50–57. http://dx.doi.org/10.1109/tmech.2004.823875.

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29

Hsieh, Suhua, and Ying-Jer Shih. "Automated guided vehicle systems and their Petri-net properties." Journal of Intelligent Manufacturing 3, no. 6 (December 1992): 379–90. http://dx.doi.org/10.1007/bf01473533.

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30

Arifin, Robert, and Pius J. Egbelu. "Determination of vehicle requirements in automated guided vehicle systems: A statistical approach." Production Planning & Control 11, no. 3 (January 2000): 258–70. http://dx.doi.org/10.1080/095372800232225.

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31

Gobal, S. L., and R. G. Kasilingam. "A simulation model for estimating vehicle requirements in automated guided vehicle systems." Computers & Industrial Engineering 21, no. 1-4 (January 1991): 623–27. http://dx.doi.org/10.1016/0360-8352(91)90163-z.

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32

KING, RUSSELL E., and CARL WILSON. "A review of automated guided-vehicle systems design and scheduling." Production Planning & Control 2, no. 1 (January 1991): 44–51. http://dx.doi.org/10.1080/09537289108919329.

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33

GASKINS, ROBERT J., J. M. A. TANCHOCO, and FATANEH TAGHABONI. "Virtual flow paths for free-ranging automated guided vehicle systems." International Journal of Production Research 27, no. 1 (January 1989): 91–100. http://dx.doi.org/10.1080/00207548908942532.

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34

KOUVELIS, PANAGIOTIS, GENARO J. GUTIERREZ, and WEN-CHYUAN CHIANG. "Heuristic unidirectional flowpath design approaches for automated guided vehicle systems." International Journal of Production Research 30, no. 6 (June 1992): 1327–51. http://dx.doi.org/10.1080/00207549208942960.

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35

Jeong, Byung Ho, and Sabah U. Randhawa. "A multi-attribute dispatching rule for automated guided vehicle systems." International Journal of Production Research 39, no. 13 (January 2001): 2817–32. http://dx.doi.org/10.1080/00207540110051860.

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36

Farling, B. E., C. T. Mosier, and F. Mahmoodi. "Analysis of automated guided vehicle configurations in flexible manufacturing systems." International Journal of Production Research 39, no. 18 (January 2001): 4239–60. http://dx.doi.org/10.1080/00207540110072957.

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37

Norman, Susan K., and Donald E. Scheck. "Design of a Simulation Package for Automated Guided Vehicle Systems." Computers & Industrial Engineering 11, no. 1-4 (January 1986): 401–5. http://dx.doi.org/10.1016/0360-8352(86)90120-8.

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38

Salehipour, Amir, and Mohammad Mehdi Sepehri. "Optimal location of workstations in tandem automated-guided vehicle systems." International Journal of Advanced Manufacturing Technology 72, no. 9-12 (June 2014): 1429–38. http://dx.doi.org/10.1007/s00170-014-5678-x.

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39

BISCHAK, DIANE P., and KEITH B. STEVENS. "An evaluation of the tandem configuration automated guided vehicle system." Production Planning & Control 6, no. 5 (September 1995): 438–44. http://dx.doi.org/10.1080/09537289508930301.

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40

Witczak, Marcin, Ralf Stetter, Mariusz Buciakowski, Didier Theilliol, and Norbert Kukurowski. "Design of diagnostic estimators for an automated guided vehicle." IFAC-PapersOnLine 51, no. 24 (2018): 1004–9. http://dx.doi.org/10.1016/j.ifacol.2018.09.710.

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41

BOZER, YAVUZ A., and MANDYAM M. SRINIVASAN. "Tandem Configurations for Automated Guided Vehicle Systems and the Analysis of Single Vehicle Loops." IIE Transactions 23, no. 1 (March 1991): 72–82. http://dx.doi.org/10.1080/07408179108963842.

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42

Liu, Chin-I., and P. A. Ioannou. "Petri Net Modeling and Analysis of Automated Container Terminal Using Automated Guided Vehicle Systems." Transportation Research Record: Journal of the Transportation Research Board 1782, no. 1 (January 2002): 73–83. http://dx.doi.org/10.3141/1782-09.

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43

Li, Q., A. C. Adriaansen, J. T. Udding, and A. Y. Pogromsky. "Design and Control of Automated Guided Vehicle Systems: A Case Study." IFAC Proceedings Volumes 44, no. 1 (January 2011): 13852–57. http://dx.doi.org/10.3182/20110828-6-it-1002.01232.

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44

Katz, Z., J. Asbury, and R. Weill. "Communication and Mapping Systems for a Free-Ranging Automated Guided Vehicle." CIRP Annals 40, no. 1 (1991): 471–74. http://dx.doi.org/10.1016/s0007-8506(07)62032-0.

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45

Huang, C. "Design of material transportation system for tandem automated guided vehicle systems." International Journal of Production Research 35, no. 4 (April 1997): 943–53. http://dx.doi.org/10.1080/002075497195461.

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46

Shen, Yu-Cheng, and John E. Kobza. "A dispatching-rule-based algorithm for automated guided vehicle systems design." Production Planning & Control 9, no. 1 (January 1998): 47–59. http://dx.doi.org/10.1080/095372898234514.

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47

Chiba, Ryosuke, Tamio Arai, and Jun Ota. "Integrated Design for Automated Guided Vehicle Systems Using Cooperative Co-evolution." Advanced Robotics 24, no. 1-2 (January 2010): 25–45. http://dx.doi.org/10.1163/016918609x12585527087776.

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48

TAGHABONI, FATANEH, and J. M. A. TANCHOCO. "A LISP-based controller for free-ranging automated guided vehicle systems." International Journal of Production Research 26, no. 2 (February 1988): 173–88. http://dx.doi.org/10.1080/00207548808947852.

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49

Kumar, Ravindra, Abid Haleem, Suresh K. Garg, and Rajesh K. Singh. "Automated guided vehicle configurations in flexible manufacturing systems: a comparative study." International Journal of Industrial and Systems Engineering 21, no. 2 (2015): 207. http://dx.doi.org/10.1504/ijise.2015.071510.

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50

Langevin, André, Diane Riopel, Gilles Savard, and Rachel Bachmann. "A Multi-Commodity Network Design Approach For Automated Guided Vehicle Systems." INFOR: Information Systems and Operational Research 42, no. 2 (May 2004): 113–23. http://dx.doi.org/10.1080/03155986.2004.11732695.

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