- Mobile robot fleet controlled directly
- Autonomous pallet / container transport without conveyor technology
- Economical supply / disposal of workplaces
- Freely movable routes, maximum flexibility (no induction loop, etc.)
Autonomous transport robots are used to move various goods and materials automatically and without personnel. They are becoming more and more important in today's logistics, especially as they are becoming more and more intelligent, flexible and secure.
They not only transport the goods on strictly defined routes from A to B, but also move independently of floor-based control systems in the warehouse and production. By optimizing routes, you ensure lean processes in your company.
This not only makes your logistics more efficient, but also relieves your employees by reducing walking distances.
Autonomous transport robots (Automated Guided Vehicle = AGV) are very versatile. They can be used in work areas with pedestrian traffic as well as in fully automated work areas and are extremely flexible in their design.
Compared to manual transport with forklifts or continuous conveyors (pallet / container conveyor technology), an autonomous transport robot has some advantages and is therefore used more and more frequently. Furthermore, the risk of accidents and transport damage can be reduced through the reduced forklift traffic.
Their great advantage over traditional, track-based transport systems is that they react intelligently and dynamically to the environment and can find their way between people or in changing environments. This makes expensive structural track guides, such as conveyor systems, guide wires or recessed markers, superfluous in one fell swoop.
In this way, the autonomous transport robots can be easily adapted to the respective task areas. Due to the saved personnel costs, especially in shift operation, autonomous transport robots often pay for themselves after a few years.
Up to now, driverless transport systems have required an adaptation of the environment or the specification of fixed routes - for example through optical guidelines or floor markings.
With the help of environment master data, the autonomous transport robots choose the most optimal route for themselves and decide their own route.
Detected obstacles are automatically recognized and stored in the master data. The environment data is updated in a self-learning manner so that future tasks can be processed even better.