Adaptive warning fields for warehouse AGVs

Muhammad, Naveed; Hedenberg, Klas; Åstrand, Björn

doi:10.1109/etfa45728.2021.9613565

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Bearing in mind the numerous analyzed limitations, the overcoming of which would increase the efficiency of customer service, the main goal of this paper is the selection of smart solutions for MH based on combinations of modern MH equipment and I 4.0 technology. In order to overcome some key challenges, four alternatives are discussed in this research: SAGV forklifts, AMRs, GTP and UAVs, which are supported by CWMS, CGPS, BD, DM, IIoT, AI and AR as the most significant I 4.0 technologies for MH activities [81][82][83][84][85][86][87][88][89][95][96][97][98][99][100][101][102][103][104][105][106][107][108]. Each alternative is analyzed in terms of its advantages and disadvantages from the perspective of MH in logistics center and are evaluated based on fourteen criteria.…”

Section: Discussionmentioning

confidence: 99%

“…AMRs symbolize a fully automated system that can independently complete intralogistics activities because it uses onboard sensors and processors for automatic MH without the need for physical controls [96]. Routing in the AMR system is accomplished using CGPS, which traces the asset's location in real time and provides coordinates for further movement [97]. In contrast to traditional AGV systems with a fixed route, AMRs have much greater flexibility because of AI and AR support [89].…”

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

“…BD collects data on completed assignments and routes as a base for DM to optimize the route to the next warehouse assignment [86,87]. Because AMRs do not have the possibility of active pick-up and disposal of material, IIoT technology provides adequate information about the engagement of pick-up mechanisms, such as robotic arms, as well as collaboration with other equipment, such as cranes [78,79,[97][98][99][100]. Thus, upon completion of the warehouse task, the CWMS data provided by the BD is forwarded to the auxiliary equipment, which, at that moment, engages in the next MH position [84].…”

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

“…Thus, upon completion of the warehouse task, the CWMS data provided by the BD is forwarded to the auxiliary equipment, which, at that moment, engages in the next MH position [84]. As with SAGVs, the basic task of CWMS is to provide all the necessary information that is a prerequisite for performing the warehouse assignment, which is related to the position of the external transportation vehicle, its start and end times in MH activities, the contents of deliveries, and the process of performing intralogistics activities [97]. AMRs are a contemporary technology that provides additional benefits in implementation due to its simple integration into the existing storage system and effective engagement when other technologies fail in MH activities [98,100].…”

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

“…As a result, robotic arms, cranes or other devices that can provide pick-up of the load need to be used on transshipment decks [55,56]. Lower capacity and speeds, including longer battery charging times for AMRs compared to AGV forklifts, are some of the additional limitations that require consideration when employing them for MH activities [97][98][99].…”

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

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Smart Material Handling Solutions for City Logistics Systems

et al. 2023

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Globalization, the growth of the world population, urbanization and the growth of the volume of the flow of goods have generated numerous problems in city logistics (CL). The opportunity to solve them is found in various fields by defining and implementing initiatives, concepts, measures, modern technologies and scenarios. The efficiency of the solution largely depends on the efficiency of logistics centers, which is one of the key subsystems of CL. The requirements for the reliable delivery of goods to customers in urban areas are conditioned by the efficiency their order fulfillment in logistics centers. Therefore, optimizing material handling (MH) time and costs aimed at reducing delivery errors, minimizing damage to goods and increasing customer service efficiency is directly conditioned by the automation of MH in logistics centers. Accordingly, this paper aims to rank and select smart MH solutions in logistics centers where deliveries are prepared for the supply of the city area. This paper proposes four smart solutions for a real company, and fourteen criteria are selected for the evaluation. A new hybrid Multi-Criteria Decision-Making model that combines the Fuzzy Analytic Hierarchy Process method, used to determine the criteria weights, and the Fuzzy COmprehensive distance-Based RAnking (FCOBRA) method, used to rank the alternatives, is proposed. The application of the model shows that the best alternative is the implementation of an autonomous forklift, which can greatly automate logistics activities and reduce the rate of delivery errors. The main contributions of this research are the definition of smart solutions, a framework for their evaluation and a new model for their ranking.

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Section: Discussionmentioning

confidence: 99%

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

Section: Defining Alternative Smart Solutionsmentioning

confidence: 99%

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Smart Material Handling Solutions for City Logistics Systems

et al. 2023

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Identifying Worker Motion Through a Manufacturing Plant: A Finite Automaton Model

Yang,

Bhat,

Ren

et al. 2024

2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)

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Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review

Belter,

Hering,

Weichbroth

2023

Applied Sciences

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Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.

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Adaptive warning fields for warehouse AGVs

Cited by 4 publications

References 9 publications

Smart Material Handling Solutions for City Logistics Systems

Smart Material Handling Solutions for City Logistics Systems

Identifying Worker Motion Through a Manufacturing Plant: A Finite Automaton Model

Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review

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