Intralogistics
Assembly Line
Super Payload AGV Animation
Sereact Lens | Case Study | AI-based inventory management
RIL case picking overview
🧠🤖 Optimising Intralogistics with AI
In its production facilities in Barntrup, KEB operates the in-house transport system AGILOX, which is designed specifically for intralogistics tasks. The AGILOX system is comprised of a swarm (union) of smart automated guided vehicles (AGVs), working collaboratively to transport items throughout KEB’s warehouses.
In AutoQML – a project that develops solution approaches for linking quantum computing and machine learning – KEBs primary objective is to devise a machine learning solution capable of monitoring vehicle status and predicting potential failures. This aligns with KEBs larger objective of facilitating the broader transition to quantum computing in the future, by supporting research institutes with practical, real-world applications.
Autonomous intralogistics from indoors to outdoors for a safe and seamless logistics chain
Computer-on-Modules For Autonomous Intralogistics Vehicles
At Transpharm Logistik, however, the promotional products change frequently and come in different shapes, sizes and weights. Staff therefore have to pick them individually per recipient. Nevertheless, Transpharm Supply Chain Analyst Martin Zwiebel was tasked to optimize the pick and delivery process further. “Staff were using heavy, bulky carts to pick promotional products,” recounts Zwiebel. Equipped with tablets and supported in some cases by pick-by-light systems, they gathered the individual items from across the entire warehouse and then wheeled the cart with the complete pick to the packing department, where the promotional products were made ready for dispatch. “When looking for a faster and easier solution, it became apparent that a driverless transport system promised significant advantages,” the analyst continued. So, what was needed was an affordable robotic trolley that could autonomously find its way to the next storage bay following a predefined optimized route, and that would prove a constant and helpful companion to staff.
AI in production logistics: mastering flexibility with KUKA AIVI
AGV and AMR: What is the Actual Difference?
In logistics centers and production halls, there are always a lot of pallets, crates, mesh boxes, racks and numerous other objects that must be transported. This task can be accomplished by forklifts with human operators behind the steering wheel. Increasingly, driverless transport systems (DTS) are being used to move goods autonomously from A to B.
These driverless transport vehicles include Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). Although they both accomplish the same tasks, these abbreviations should not be used synonymously: the two vehicle types are different and each of them has specific characteristics.
The A in AGV stands for Automated, while the A in AMR stands for Autonomous: a small difference with major significance. As the name suggests, AMRs operate autonomously, for instance by evading obstacles that suddenly block their path. On the other hand, AGVs travel on fixed routes and can only accomplish pre-defined tasks by following automated instructions. In contrast, AMRs make their own decisions when a situation requires.
Highly flexible AGV solution in truck cabin production
Towards Artificial Intelligence in Warehouse Automation
GEBHARDT Fördertechnik was founded in 1952 as a mechanical engineering company and has a long-standing experience in developing and manufacturing of system solutions for intralogistics. With this broad range of knowledge GEBHARDT can deliver everything out of one hand: from planning, design, implementation and continuous support up to an optimally integrated solution for warehouse management.
When a shuttle is in motion, vibrations can occur due to used parts at the shuttle or at the high rack. These vibrations are recorded with sensors CMS01 – Gebhardt own development - and then correlated with the driving parameters which come from control units. The different sources are merged with Crosser modules on the Edge and are the basis for calculating the health status of a shuttle. In addition, the data act as input for inhouse developed predictive maintenance models. This approach minimizes the risk of failure and reduces maintenance costs.
GEBHARDT’S architecture is based on the idea of processing data directly at the edge, transferring only relevant data to the back-office system or the respective cloud solution. This saves money and allows fast processing. The data is read and processed directly at the sensors. Processing steps include time series harmonization, data enrichment or data quality improvements. The automatic learning of the system takes place in a specific step of the process chain. It was important for GEBHARDT to use standard components which can be configured easily. The software components for the implementation, execution and maintenance of the processing steps are carried out with the help of the tools from Crosser: Crosser Edge Node™ and Crosser Cloud™.