AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.
Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.
As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor detection.In fact,the floor detection task can be greatly simplified with proper data collection system design.In this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is provided.The features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests.The time delay and floor detection accuracy under different settings are discussed.Finally,floor detection is evaluated on the H20 multi-floor transportation robot.Two sensor nodes are installed on the robot at different heights.The floor detection performance with different installation heights is discussed.The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.
This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
