Thanks to rapid technological developments in robotics, various automation technologies are being applied in warehouses today. Order picking, as a key process in warehouse operations, has drawn attention in academia and practice for decades. In addition to many studies dedicated to manual and fully automated order picking, efforts have also been made to study semi-automated warehouses in which humans and robots collaborate. However, these studies mostly focused on system efficiency and ignored ergonomic aspects. Order picking was confirmed as a labor-intensive process in an environment in which workers are at a high risk of developing health problems. Therefore, this study addresses the investigation of physical human working conditions in both manual and robot-assisted order picking systems via real-life laboratory experiments and simulation modeling. We used a motion capture system to assess human working postures when working with and without robot assistance. In addition, we estimated the daily workload by applying the energy expenditure concept. Using simulation experiments, we were able to extend the results to various practical scenarios with different design variables, for example warehouse layouts, order sizes, and human-robot team configuration. Our preliminary results reveal that human-robot collaboration can reduce human workload. Posture evaluation also shows a slight improvement. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase.

This paper presents automated storage systems with shuttles integrated with hoisted carriage for successful application in intralogistics. The first part of the paper presents classic and advanced AVS/RS along with specific intralogistics automation systems known as AutoStore from Swisslog and Skypod from Exotec. The second part of the paper focuses on an advanced system with shuttle vehicles capable of serving multiple tiers of the storage rack. An analytical model for the shuttle vehicles capable of serving multiple tiers of the storage rack is presented, which is based on (i) the sequences of acceleration, constant velocity and deceleration, and (ii) randomised assignment policy. Based on the presented model, the expected Single Command (SC) and Dual Command (DC) travel (cycle) time as well as the throughput performance of the shuttle vehicles capable of serving several tiers of warehouse, could be calculated. A programme code in MATLAB has been presented for the computation of throughput performances of automated storage systems with shuttles integrated with hoisted carriage capable of serving several tiers of the storage rack.

The rise of e-commerce, which demands solutions for small order sizes, large product assortment, short delivery times, and variable order quantity has created a need for more advanced warehousing and order-picking systems. The advancements in collaborative robotics have made it possible to automate intralogistics processes, especially the order-picking systems. If the items to be order-picked are stored in small containers, called bins, the process is known as bin-picking. The processes of bin-picking are still mainly manual due to the adaptability, dexterity, and pace of human hands. However, with the emerging technologies, the gap between human and robot dexterity is getting thinner. The integrators of robotic technologies are therefore faced with the challenge of choosing an appropriate robotic system for bin-picking. There is a lack of standardized procedures, which are designed to process such decisions easier. In our research paper, we propose an improved robotic version of the Box and Blocks Test (BBT), which provides a quick evaluation of a complete robotic bin-picking system. Using our improved robot-adapted BBT protocol, we evaluate a sample robotic bin-picking system, consisting of a collaborative robot, a 3D vision system and three types of robotic grippers, comparing the bin-picking performance of each configuration.

This paper presents automated storage systems with shuttles integrated with hoisted carriage for successful application in intralogistics. The first part of the paper presents classic and advanced AVS/RS along with specific intralogistics automation systems known as AutoStore from Swisslog and Skypod from Exotec. The second part of the paper focuses on an advanced system with shuttle vehicles capable of serving multiple tiers of the storage rack. An analytical model for the shuttle vehicles capable of serving multiple tiers of the storage rack is presented, which is based on (i) the sequences of acceleration, constant velocity and deceleration, and (ii) randomised assignment policy. Based on the presented model, the expected Single Command (SC) and Dual Command (DC) travel (cycle) time as well as the throughput performance of the shuttle vehicles capable of serving several tiers of warehouse, could be calculated. A programme code in MATLAB has been presented for the computation of throughput performances of automated storage systems with shuttles integrated with hoisted carriage capable of serving several tiers of the storage rack.

Product assembly is often one of the last steps in the production process. Product assembly is often carried out by workers (assemblers) rather than robots, as it is generally challenging to adapt automation to any product. When assembling complex products, it can take a long time before the assembler masters all the steps and can assemble the product independently. Training time has no added value; therefore, it should be reduced as much as possible. This paper presents a custom-developed system that enables the guided assembly of complex and diverse products using modern technologies. The system is based on pick-to-light (PTL) modules, used primarily in logistics as an additional aid in the order picking process, and Computer Vision technology. The designed system includes a personal computer (PC), several custom-developed PTL modules and a USB camera. The PC with a touchscreen visualizes the assembly process and allows the assembler to interact with the system. The developed PC application guides the operator through the assembly process by showing all the necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked out of the bin, first by checking that the correct pushbutton on the PTL module has been pressed and second by using a camera with a Computer Vision algorithm. The paper is supported by a use case demonstrating that the proposed system reduces the assembly time of the used product. The presented solution is scalable and flexible as it can be easily adapted to show the assembly steps of another product.

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.

Stochastic nature-inspired population-based algorithms are very powerful tools for solving stationary and deterministic, NP-hard optimization problems. These algorithms have rarely been applied to real-world dynamic and uncertain optimization due to their complexity. In this paper, this kind of algorithms were ported onto real hardware (i.e., the velocity controller of a one degree of freedom robot mechanism), where they were used to control the behavior of a non-linear system online. This means that the feedback response from the system must be less than 5 ms. Due to the complexity of the fitness function evaluation, a surrogate linear model was used, implemented as a single-layer artificial neural network, consisting of two phases: learning and simulation. In the first phase, the model of the nonlinear plant is learned during online operation, while in the second, the value of the fitness function needed by the optimization algorithms is predicted. Six algorithms were compared with the PI-controller in our experimental work. This were: classical evolution strategies, contemporary evolution strategies, differential evolution, self-adaptive differential evolution, particle swarm optimization, and the bat algorithm. The results showed that the algorithms outperformed PI-controller in the sense of stability, flexibility and adaptability. (C) 2020 Elsevier B.V. All rights reserved.

The increasing number of sensors used in industrial environments generate a need for systems, which are capable of processing multiple data streams and converting them into usable information. In this article, we present the development and usage of a real-time measuring and analysis system designed for use with various sensors in industrial environment. The focus of this paper is a simple encoding protocol running on a digital signal controller which transmits data at high speeds over serial communication interface to a personal computer, where they are decoded. Data are being refreshed and graphed on screen at sampling times up to 1.5 milliseconds in a C# application, which enables fast and efficient data logging. Digital signal controller in combination with wireless modules allows multiple sensors to be used in same room with a central decoding unit. The proposed system could be used in an industrial environment where various sensors with high sampling rates are used and fast data logging is required. The proposed protocol was tested on a robotic mechanism.

POVZETEK Litij-ionske baterije so s svojimi odličnimi lastnostmi skoraj povsem nadomestile ostale tipe baterij. Zaradi visoke nazivne napetosti so primerne za zaporedno vezavo v baterijske sklope, vendar se z naraščanjem števila celic v paketu zvǐsuje kompleksnost sistema ter posledično možnost napačnega delovanja. V ta namen se za nadzor nad baterijskim paketom in izravnavo napetosti na posameznih celicah, ki so posledica kemijske sestave, uporabljajo baterijski nadzorni sistemi (angl. battery management system, kraǰse BMS). V tem članku je predstavljena izdelava prototipnega BMS sistema, opisana je simulacija sistema v simulacijskem okolju MATLAB/Simulink, kakor tudi fizična izdelava elektronskega vezja. Določena je nadzorna in regulacijska funkcija blokovno programiranega digitalnega signalnega krmilnika TMS320F28335. Predstavljeni so rezultati nadzorovanja realnega delovanja aktivnega BMS sistema na treh zaporedno vezanih litij-ionskih celicah, ki kažejo vzpodbudno delovanje zasnovanega BMS sistema.