8. 六月 2022

TGW荣获著名的VDI物流创新奖

  • Rovolution拣选机器人及其数字孪生技术荣获VDI颁发的社会生产与物流大奖
  • 凭借人工智能,机器人解决方案可以自主决策
  • 通过数字孪生技术,其动作可见且可理解

(Marchtrenk, 8 June 2022) With this award, the Association of German Engineers (Verein Deutscher Ingenieure, VDI) honours companies' outstanding achievements in terms of logistics innovation. TGW received the renowned Innovation Award for the self-learning picking robot Rovolution and its digital twin.

The award was presented by Gregor Blauermel (Chair of the Jury), Prof. Johannes Fottner (Deputy Chair of the Jury) and Jean Haeffs (Managing Director of the VDI Society Production and Logistics). "Rovolution receiving the Logistics Innovation Award is a magnificent validation of our development and innovation strategy, which focuses in particular on robotics, software and digitalisation," points out Christoph Wolkerstorfer, Chief Sales Officer of the TGW Logistics Group.

Rovolution: intelligent and self-learning

Rovolution is based on insights gained from artificial intelligence, cognitive robotics and image recognition. The picking robot makes autonomous decisions about how to handle a situation. The intelligent technology continues to learn with every gripping operation, gathers experience and is capable of recognising patterns.

"With Rovolution, TGW is taking automation one step further to offer a solution suited to the challenges of the market," says Christoph Wolkerstorfer.

Digital twin: optimal performance, higher transparency

Furthermore, TGW has developed a digital twin for Rovolution – that is, a complete digital representation that is connected to the physical installation in real time and grows with it. The digital twin renders behaviour and overall context visible, comprehensible and predictable. It helps you analyse data, learn from them and visualise them in 3D models.

This makes it possible not only to monitor the Rovolution's current condition, but also to look back in time and identify causes of unexpected events by means of a replay function. It is even possible to look into the future: within the context of Predictive Maintenance, for example. "With the digital twin, users benefit from optimum transparency, increased productivity and a reduction of operating costs," explains Christoph Wolkerstorfer.

发现TGW