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In contemporary warehouse logistics, the demand for efficient and precise inventory management is increasingly critical, yet traditional Radio Frequency Identification (RFID)-based systems often falter due to static antenna configurations that limit tag detection efficacy in complex environments with diverse object arrangements. Addressing this challenge, we introduce an advanced RFID-based inventory robot that integrates a 3-degree-of-freedom (3DOF) manipulator with vision-assisted dynamic antenna positioning to optimize tag detection performance. This autonomous system leverages a pretrained You Only Look Once (YOLO) model to detect objects in real time, employing forward and inverse kinematics to dynamically orient the RFID antenna toward identified items. The manipulator subsequently executes a tailored circular scanning motion, ensuring comprehensive coverage of each object’s surface and maximizing RFID tag readability. To evaluate the system’s efficacy, we conducted a comparative analysis of three scanning strategies: (1) a conventional fixed antenna approach, (2) a predefined path strategy with preprogrammed manipulator movements, and (3) our proposed vision-assisted dynamic positioning method. Experimental results, derived from controlled laboratory tests and Gazebo-based simulations, unequivocally demonstrate the superiority of the dynamic positioning approach. This method achieved detection rates of up to 98.0% across varied shelf heights and spatial distributions, significantly outperforming the fixed antenna (21.6%) and predefined path (88.5%) strategies, particularly in multitiered and cluttered settings. Furthermore, the approach balances energy efficiency, consuming 22.1 Wh per mission—marginally higher than the fixed antenna (18.2 Wh) but 9.8% less than predefined paths (24.5 Wh). By overcoming the limitations of static and preprogrammed systems, our robot offers a scalable, adaptable solution poised to elevate warehouse automation in the era of Industry 4.0.

The world of retailing is changing rapidly, and much of that change has been enabled by customer-interfacing retail technologies. This commentary offers a framework for classifying technologies, based on their primary influence on a customer’s purchase journey – in the pre-purchase stage, needs management and search engagement technologies; in purchase stage, purchase transaction and physical acquisition technologies; and in the post-purchase stage, follow-up service and loyalty management technologies. We then discuss and classify forty recent retail technologies according to this framework. Finally, we identify areas that offer great potential for further research on retail technology.

This study proposes a shopping assistant robot, called CartBot, to facilitate the grocery shopping experience for customers/shoppers. A grocery store environment can be complex and confusing to customers. Therefore, the main aim is to assist customers in navigating this environment efficiently while carrying their purchased items, and hence improve the overall shopping experience and reduce shopping time. To achieve this, a unified framework for implementing path planning and collision avoidance in a supermarket environment is proposed. Here, with a shopping list as the input, an efficient (or near-optimal) global path is generated to complete the shopping. Then, a real-time local planner is proposed to navigate this path while avoiding any obstacles that are encountered. Various features/strategies to facilitate navigation and obstacle interactions are also addressed in this work. Simulation studies of CartBot are carried out in a grocery store environment along with other CartBots, employees, and human shoppers to validate the performance of the proposed approach.

In automated warehousing scenarios, mobile UHF RFID robots typically operate along preset fixed paths to collect basic information from goods tags. They lack the ability to perceive shelf layouts and goods distribution, leading to problems such as missing reads and low inventory efficiency. To address this issue, this paper proposes a cross-layer sensing method for mobile UHF RFID robot reading states based on multiple linear regression-orthogonal least squares (MLR-OLS) and random forest. For shelf state sensing, a position sensing model is constructed based on the physical layer, and MLR-OLS is used to estimate shelf positions and interaction time. For good state sensing, combining physical layer and MAC layer features, a K-means-based tag density classification method and a missing tag count estimation algorithm based on frame states and random forest are proposed to realize the estimation of goods distribution and the number of missing goods. On this basis, according to the read state sensing results, this paper further proposes an adaptive reading strategy for RFID robots to perform targeted reading on missing goods. Experimental results show that when the robot is moving at medium and low speeds, the proposed method can achieve centimeter-level shelf positioning accuracy and exhibit high reliability in goods distribution sensing and missing goods count estimation, and the adaptive reading strategy can significantly improve the goods read rate. This paper realizes cross-layer sensing and read optimization of the RFID robot system, providing a theoretical basis and technical route for the application of mobile UHF RFID robot systems.

The increasing adoption of artificial intelligence (AI) in intelligent healthcare systems has elevated the demand for robust medical imaging and vision-based inventory solutions. For an intelligent healthcare inventory system, accurate recognition and classification of medical items, including medicines and emergency supplies, are crucial for ensuring inventory integrity and timely access to life-saving resources. This study presents a hybrid deep learning framework, EfficientDet-BiFormer-ResNet, that integrates three specialized components: EfficientDet’s Bidirectional Feature Pyramid Network (BiFPN) for scalable multi-scale object detection, BiFormer’s bi-level routing attention for context-aware spatial refinement, and ResNet-18 enhanced with triplet loss and Online Hard Negative Mining (OHNM) for fine-grained classification. The model was trained and validated on a custom healthcare inventory dataset comprising over 5000 images collected under diverse lighting, occlusion, and arrangement conditions. Quantitative evaluations demonstrated that the proposed system achieved a mean average precision (mAP@0.5:0.95) of 83.2% and a top-1 classification accuracy of 94.7%, outperforming conventional models such as YOLO, SSD, and Mask R-CNN. The framework excelled in recognizing visually similar, occluded, and small-scale medical items. This work advances real-time medical item detection in healthcare by providing an AI-enabled, clinically relevant vision system for medical inventory management.

Purpose Technology revolutionises the tourism industry and determines the strategy and competitiveness of tourism organisations and destinations. This paper aims to explore the transformational and disruptive nature of technology for tourism. Design/methodology/approach This paper is based on systematic research. Findings Technology innovations bring the entire range of stakeholders together in tourism service ecosystems. Technology-empowered tourism experiences increasingly support travellers to co-create value throughout all stages of travel. Ambient Intelligence (AmI) Tourism (2020-future) is driven by a range of disruptive technologies. Inevitably smart environments transform industry structures, processes and practices, having disruptive impacts for service innovation, strategy, management, marketing and competitiveness of everybody involved. Originality/value The paper synthesises developments in technology for tourism and proposes a future perspective.

Purpose The combination of the latest advancements in information and communication technologies with the latest developments in AutoID technologies, especially radio frequency identification (RFID), brings the possibility of high-resolution, item-level visibility of the entire supply chain. In the particular case of retail, visibility of both the stock count and item location in the shop floor is crucial not only for an effective management of the retail supply chain but also for physical retail stores to compete with online retailers. The purpose of this paper is to propose an autonomous robot that can perform stock-taking using RFID for item-level identification much more accurately and efficiently than the traditional method of using human operators with RFID handheld readers. Design/methodology/approach This work follows the design science research methodology. The paper highlights a required improvement for an RFID inventory robot. The design hypothesis leads to a novel algorithm. Then the cycle of development and evaluation is iterated several times. Finally, conclusions are derived and a new basis for further development is provided. Findings An autonomous robot for stock-taking is proven feasible. By applying a proper navigation strategy, coupled to the stream of identifications, the accuracy, precision, consistency and time to complete stock-taking are significantly better than doing the same task manually. Research limitations/implications The main limitation of this work is the unavailability of data to analyze the actual impact on the correction of inventory record inaccuracy and its subsequent implications for the supply chain management. Nonetheless, it is shown that figures of actual stock-tacking procedures can be significantly improved. Originality/value This paper discloses the potential of deploying an inventory robot in the supply chain. The robot is called to be a key source of inventory data conforming supply chain management 4.0 and omnichannel retail.

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators—such as shelf access frequency and the total processing time of the RMFS—are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.

PurposeDespite its potential, warehouse managers still struggle to successfully assimilate autonomous mobile robots (AMRs) in their operations. This paper means to identify the moderating factors of AMR assimilation for production warehouses that influence the digital transformation of their intralogistics via AMRs.Design/methodology/approachDrawing on innovation of assimilation theory (IAT), this study followed an explorative approach using the principles of the case study method in business research. The cases comprised of four AMR end users and six AMR service providers. Data were collected through semi-structured interviews.FindingsFour clusters of moderators that affect each stage of AMR assimilation were identified. These clusters include organizational attributes of end users (i.e. production warehouses), service attributes of service providers, technology attributes of AMRs and relational attributes between the AMR service providers and the AMR end users.Originality/valueThe authors extend the IAT framework by identifying various moderating factors between different stages of the AMR assimilation process. To the authors' knowledge, this is the first study to introduce the perspective of AMR end users in conjunction with AMR service providers to the “Industry 4.0” technology assimilation literature. The study propositions regarding these factors guide future intralogistics and AMR research.

Robot manipulators are growing more widely employed in the retail market, mostly for warehousing, but automating them in-store logistics processes is still a difficult task. Supermarkets and large retail stores face many challenges: shortages, handling, and placement of a single product on shelves. Various issues needed to be considered to develop a robot which can manipulate products of different sizes, shapes, and weight in limited spaces on shelves. The aim of this article is to design and develop a system to address the issues of shortage, identification, moving, and placements of products in supermarkets by properly incorporating database, camera vision, and line following mobile manipulator. A four-wheeled differential drive mobile robot was designed and developed which has a 5 DOF robotic manipulator on it. The line following technique is used to move it around the warehouse. The barcode recognition technique for the localization of product sections and object detection using SIFT is successfully and efficiently employed. The demonstration of the usefulness of the method was shown by carrying out experiments in a relevant environment which imitates a real supermarket.