Automated Guided Vehicle (AGV)
Assembly Line
🧠🤖 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.
AGVs for Automating Heavy Load Manufacturing Conveyance
For right now’s heavy producers, conveyance automation methods should be extraordinarily sturdy and able to transporting high-capacity payloads, but additionally ship the excessive ranges of flexibility, security and scalability anticipated from right now’s cell robotic methods. Trendy automated guided automobiles can do exactly that.
Trendy automated guided automobiles mix the capabilities set of autonomous cell robots – flexibility, security and scalability – with the load capability of towline conveyors. As such, they supply producers with the most effective of each worlds, a cheap, versatile, heavy load conveyance answer for manufacturing construct traces designed for manufacturing as it’s performed right now, and that may meet the manufacturing calls for of tomorrow.
Autonomous intralogistics from indoors to outdoors for a safe and seamless logistics chain
A simulation-based approach to design an automated high-mix low-volume manufacturing system
In this paper, we address the profit optimization problem of an automated high-mix low-volume manufacturing system, which originates from a real-world problem at our industry partner. The manufacturing system includes buffer units from which jobs are automatically transported to workstations, i.e., using automated material handling devices. We consider three different automation concepts for the system: (1) a configuration with parallel buffers and a dedicated robot to work them, (2) a configuration that employs shared buffers that are tended to by automated guided vehicles (AGVs), and (3) a proposed hybrid configuration that takes elements of both aforementioned configurations. We propose a simulation-based approach, which uses simulated-annealing (SA), enriched with the reduced variable neighborhood search (RVNS), to determine the best system configuration for a high-mix, low-volume manufacturer. Decisions concern the choice of automation equipment and the capacity of both parallel and shared buffers. We illustrate the efficacy of the proposed hybrid concept and the proposed SA-RVNS approach with an industry case study using real-world data from our industry partner. Our analysis shows that the proposed concept increases the profit by around 10–30% compared to the others, and the AGV travel time plays an important factor in the proposed concept to yield its true potential.
Action-limited, multimodal deep Q learning for AGV fleet route planning
In traditional operating models, a navigation system completes all calculations i.e., the shortest path planning in a static environment, before the AGVs start moving. However, due to constant incoming offers, changes in vehicle availability, etc., this creates a huge and intractable optimization problem. Meanwhile, an optimal navigation strategy for an AGV fleet cannot be achieved if it fails to consider the fleet and delivery situation in real-time. Such dynamic route planning is more realistic and must have the ability to autonomously learn the complex environments. Deep Q network (DQN), that inherits the capabilities of deep learning and reinforcement learning, provides a framework that is well prepared to make decisions for discrete motion sequence problems.
MiR robots improve productivity at Faurecia
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.
Start-ups Powering New Era of Industrial Robotics
Much of the bottleneck to achieving automation in manufacturing relates to limitations in the current programming model of industrial robotics. Programming is done in languages proprietary to each robotic hardware OEM – languages “straight from the 80s” as one industry executive put it.
There are a limited number of specialists who are proficient in these languages. Given the rarity of the expertise involved, as well as the time it takes to program a robot, robotics application development typically costs three times as much as the hardware for a given installation.