The Mercedes-Benz plant in Düsseldorf uses a robot dog called "Aris" and a drone as hard-working little helpers in production. They take on routine tasks and are intended to prevent system failures.
Both the robot dog and the drone are part of the digitalization strategy at the van plant in Düsseldorf. Around 5,500 employees there produce the closed models of the Mercedes-Benz Sprinter and eSprinter, i.e. both the diesel and electric versions of the van.
In future, the plant will increasingly use autonomous robot systems for predictive and preventive maintenance and in logistics. This is intended to support employees in their daily tasks and create further efficiencies. Both the robot dog and the autonomous drone can be integrated into higher-level cloud applications that enable interaction with each other and, in future, with other robots - even across plants.
Let's start with "Aris": the robot dog already performs various tasks at the plant, some of which are supported by the use of artificial intelligence (AI). These include the routine checking and recording of analog dial gauges on certain systems and machines. In addition, "Aris" is equipped with technology that can detect unusual noises and visualize them on a site plan.
Corresponding noise anomalies can, for example, indicate compressed air leaks, which can lead to a high energy loss in the plant, or be an indicator of an impending system failure. According to Mercedes-Benz Vans, this can prevent around 60 percent of the energy loss that would be caused by such leaks. This leads to energy cost savings in the six-figure range per year. And if "Aris" detects an impending system failure, it can be averted in good time through targeted repairs.
As "Aris" can also climb stairs and thus move completely autonomously in production, it could also be used for other purposes in the future. For example, "Aris" could check escape routes or create a digital twin of the plant.
The drone also already has very specific tasks: It is to carry out empties counts on the plant premises. This works by using trained AI software to recognize and count load carriers based on their dimensions, outlines and topology. This should free up time for employees who previously had to carry out these counts for more important tasks.
