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.

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.

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.

Po pandemiji COVID-19 svet ponovno pretresajo izredni dogodki, tokrat v povezavi z ukrajinsko krizo. Ta se odraža na različnih področjih naših življenj, še najbolj pa se v Sloveniji odraža v visokih cenah življenjskih potrebščin in energentov. Zaradi visokih cen le-teh, gnojil in pomanjkanja ter omejitve izvoza nekaterih dobrin so se cene hrane bistveno zvišale glede na raven pred ukrajinsko krizo. Čeprav zavržena količina hrane na prebivalca po podatkih Statističnega Urada Republike Slovenije (SURS) za leto 2020 še vedno dosega okoli 68 kg na prebivalca na leto, se potrošniki začenjamo zavedati pomena dostopa do kakovostne hrane. Slovenija je že tradicionalno orientirana v pridelavo lokalnih živil, zadnja kriza s pandemijo COVID-19 pa je pomen lokalne samooskrbe samo še poglobila. Potrošniki se zavedajo, da so, zaradi prisotnih pretresov in nestabilnosti na trgih, lokalno pridelana živila vedno pomembnejša. S pojavom spletnih tržnic, ki so se razmahnile v času pandemije COVID-19, pa smo naredili korak bližje k digitalizaciji v kmetijstvu. Prav tako nas je zanimalo, ali se potrošniki sedaj bolj odločajo za nakup lokalnih živil preko spletnih tržnic.V članku predstavljamo raziskavo, izvedeno v oktobru 2022, s katero smo želeli preveriti, kakšen je odnos do lokalno pridelane hrane v Sloveniji v času ukrajinske krize in ali so se, glede na predhodne raziskave glede na čas pandemije COVID-19, navade kaj spremenile.