Look, look, the order picker goes around - oh, oh, oh....
As a self-learning robot system, the "Smart Item Picking" system unveiled by Bosch Rexroth at HANNOVER MESSE 2022 automates manual, labor-intensive picking tasks in intralogistics.4 May 2022
At the latest with the Corona pandemic, online shopping has received an arguably irreversible boost. Every day, millions of customers order a wide variety of goods, which up to the present day have essentially been staged and made ready for dispatch by human order pickers on the basis of the respective order from the warehouse. Of course, there has long been machine support in the form of sorting systems and also robots that assist the order pickers. The problem, however, is a certain rigidity of the systems used to date. Once programmed, they often have difficulty recognizing and handling new goods and products independently. But work is being done on this. Bosch Rexroth, for example, one of the world's leading suppliers of drive and control technologies for efficient, powerful and safe motion in machines and plants. At HANNOVER MESSE 2022, the company from Lower Franconia will be celebrating the premiere of its "Smart Item Picking" robot system.
As a self-learning robot system, "Smart Item Picking" from Bosch Rexroth is said to be capable of automating manual, labor-intensive picking tasks in intralogistics. To this end, the system takes a completely model-free approach, so that it can even recognize and reliably pick objects that were previously unknown to it. Thanks to current AI methods in interaction with a warehouse management system (WMS), object recognition could be refined for each specific application and the picking strategy optimized for each individual pick. The modular design of "Smart Item Picking", based on scalable, flexible hardware and software building blocks, should not only enable the picking of a wide portfolio of different objects, it should also be fully customizable. The AI of the self-learning robot also enables reliable emptying of containers as well as volume-optimized insertion in the target container during picking. The system's current success rate is 99 percent, with a throughput of more than 600 items per hour.
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