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Automated guided vehicles position control: a systematic literature review

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Abstract

Automated Guided Vehicles (AGVs) are essential elements of manufacturing intralogistics and material handling. Improving the position accuracy along the AGV trajectory allows the vehicle to work on narrower aisles with lower error tolerance. Despite the increasing number of papers in AGVs and mobile robots’ position control research area, there is a lack of curatorial work presenting and analyzing the control strategies applied in the problem domain. Therefore, the main objective is to analyze the published researches of the past seven years on the position control of AGVs to recognize research patterns, gaps, and tendencies, outlining the research field. The paper proposes a systematic literature review to investigate the research field from the controller design perspective. Its protocol and procedures are presented in detail. Four main research topics were addressed: the control strategies used in the AGV position control problem, how the literature presents the AGV operating requirement of position accuracy, how the literature validate the proposed controller and present their results regarding the system’s position accuracy, and the technological tendencies the proposed solutions reveals. Besides, within the main topics, other points were investigated, such as the AGV application area, the considered mathematical model, the sensors and guidance system used, and the maximum payload of the vehicle and operation under different load conditions. The data synthesis shows the predominant control strategies applied to the problem and the interaction among distinct control theory areas, indicating a notable interaction of Intelligent Control techniques to the other strategies. The paper’s contributions are using a systematic literature review method over the AGV position control publications, presenting an overview of the research area, analyzing the research question topics from selected articles, and proposing a research agenda.

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Data Availability Statement

All data and material is available upon request.

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Funding

This study was financed in part by the Coordenação de Aper- feiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. W. dos Reis was financed in part by the Federal Institute of Rio de Janeiro – IFRJ, campus Volta Redonda.

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Authors and Affiliations

  1. Department of Computing, Federal University of São Carlos, São Carlos, SP, Brazil

    Wallace Pereira Neves dos Reis & Orides Morandin Junior

  2. Federal Institute of Education, Science, and Technology of Rio de Janeiro, IFRJ campus Volta Redonda, Volta Redonda, RJ, Brazil

    Wallace Pereira Neves dos Reis

  3. Federal Center for Technological Education Celso Suckow da Fonseca - CEFET-RJ, Itaguaí, RJ, Brazil

    Giselle Elias Couto

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  1. Wallace Pereira Neves dos Reis
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Wallace dos Reis and Orides Morandin Junior contributed to the study conception and design. Material preparation, and data collection were performed by Wallace dos Reis. The first draft of the manuscript was written by Wallace dos Reis and Giselle Couto, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. W. dos Reis was financed in part by the Federal Institute of Rio de Janeiro – IFRJ, campus Volta Redonda.

Appendix A Summarizing Tables of the Systematic Literature Review

Appendix A Summarizing Tables of the Systematic Literature Review

Table 5 Data extraction summarizing from feature F1 to F8: from PS01 to PS40
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Table 6 Data extraction summarizing from feature F1 to F8: from PS41 to PS80
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Table 7 Data extraction summarizing from feature F1 to F8: from PS81 to PS92. F1, F2, F3, and F4 are related to a research area overview. Those features present the publication year (F1) and authors’ country (f2), the primary study type regarding a research and validation method classification (F3), and the AGV drive configuration (F4). F5 describes the used control strategy, and F6 investigates if it has improved the position control system accuracy to answer RQ1. The AGV application area (F7) and its mathematical modeling approach (F8) are also recorded to answer RQ1.1 and RQ1.2, respectively. At the end of the table, an abbreviation glossary was included
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Table 8 Data extraction summarizing from feature F9 to F16, keeping F1 for reference: from PS01 to PS11. Regarding the AGV sensory system and aiming to answer RQ1.3, F9 identifies the AGV guidance system type while F10 recognizes the used position sensors. In addition, F11 investigates the use of a sensor fusion technique, which is listed by F12, if applicable. Besides, F13 recognizes if the sensor fusion technique improved the system accuracy. In response to RQ1.4, F14 lists the AGV maximum payload, when disclosed, and F15 examines if the authors experiment with the proposed control under a load variation condition. Lastly, F16 determines if the PSs consider the AGV operating requirements throughout the controller design process, answering RQ2. At the end of the table, an abbreviation glossary was included
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Table 9 Data extraction summarizing from feature F9 to F16, keeping F1 for reference: from PS12 to PS57. Regarding the AGV sensory system and aiming to answer RQ1.3, F9 identifies the AGV guidance system type while F10 recognizes the used position sensors. In addition, F11 investigates the use of a sensor fusion technique, which is listed by F12, if applicable. Besides, F13 recognizes if the sensor fusion technique improved the system accuracy. In response to RQ1.4, F14 lists the AGV maximum payload, when disclosed, and F15 examines if the authors experiment with the proposed control under a load variation condition. Lastly, F16 determines if the PSs consider the AGV operating requirements throughout the controller design process, answering RQ2. At the end of the table, an abbreviation glossary was included
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Table 10 Data extraction summarizing from feature F9 to F16, keeping F1 for reference: from PS58 to PS92. Regarding the AGV sensory system and aiming to answer RQ1.3, F9 identifies the AGV guidance system type while F10 recognizes the used position sensors. In addition, F11 investigates the use of a sensor fusion technique, which is listed by F12, if applicable. Besides, F13 recognizes if the sensor fusion technique improved the system accuracy. In response to RQ1.4, F14 lists the AGV maximum payload, when disclosed, and F15 examines if the authors experiment with the proposed control under a load variation condition. Lastly, F16 determines if the PSs consider the AGV operating requirements throughout the controller design process, answering RQ2. At the end of the table, an abbreviation glossary was included
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Table 11 Data extraction summarizing from feature F17 to F20, keeping F1 for reference: from PS01 to PS31. F17 and F18 extract data to answer RQ3. F17 comprises the validation method from the perspective of data presentation, comparing approaches, using performance indicators and statistical methods. Also, F18 indicates the use of statistical methods, which F17 complements by describing the statistical method, if applicable. Related to RQ4, F19 and F20 indicate, respectively, the application of multivariable control and intelligent control strategies. At the end of the table, an abbreviation glossary was included
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Table 12 Data extraction summarizing from feature F17 to F20, keeping F1 for reference: from PS32 to PS63. F17 and F18 extract data to answer RQ3. F17 comprises the validation method from the perspective of data presentation, comparing approaches, using performance indicators and statistical methods. Also, F18 indicates the use of statistical methods, which F17 complements by describing the statistical method, if applicable. Related to RQ4, F19 and F20 indicate, respectively, the application of multivariable control and intelligent control strategies. At the end of the table, an abbreviation glossary was included
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Table 13 Data extraction summarizing from feature F17 to F20, keeping F1 for reference: from PS64 to PS92. F17 and F18 extract data to answer RQ3. F17 comprises the validation method from the perspective of data presentation, comparing approaches, using performance indicators and statistical methods. Also, F18 indicates the use of statistical methods, which F17 complements by describing the statistical method, if applicable. Related to RQ4, F19 and F20 indicate, respectively, the application of multivariable control and intelligent control strategies. At the end of the table, an abbreviation glossary was included
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Reis, W.P.N.d., Couto, G.E. & Junior, O.M. Automated guided vehicles position control: a systematic literature review. J Intell Manuf 34, 1483–1545 (2023). https://doi.org/10.1007/s10845-021-01893-x

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