The reason for such rigid maintenance cycles is the lack of usable and high-quality data. Many robots are monitored with just a few sensors, which only guarantee rough data collection. However, a simple rule of thumb applies to robotics: the more data that can be collected through monitoring, the more reliable statements can be made about the future condition of the machine. This applies to all components from the gripper and bearings to the gears and drives - from pressure sensors and temperature to vibrations, current curves and ultrasound. But even if the sensors collect enough data, how can the sometimes huge amounts of data (which can be collected by ultrasonic sensors, for example) be transmitted?

Where high-resolution sensors are already in use, this problem has so far been addressed by edge AI solutions. Here, for example, an AI on the sensor cuts out the data that is deemed relevant and sends only this pre-processed data to the central processing unit or even to the cloud, where it is evaluated by another AI. With the new megatrend of "embedded AI" - which has only been possible for a few years thanks to the increasing performance of semiconductors - data evaluation can now be transferred directly to the device, making deep, comprehensive data evaluation possible without transferring the data to a server on site. This brings the idea of predictive maintenance and the avoidance of unplanned downtime within reach.

Areas of application for the technology beyond predictive or preventive maintenance include various optimization options for different applications. In the case of robots, for example, this could be an analysis of the gripper. If this no longer functions properly, a workpiece could slip and lead to a production stop. This is an old problem that has still not been optimally solved.

Cobots in the SME sector

The market is currently dominated by "large" cobots that can withstand loads of more than 10 kg. However, experts assume that the use of medium-sized and small cobots will also increase rapidly, especially in SMEs, as increasing automation will expand and improve the application possibilities of the technology.

For the cobots, the use of embedded AI offers particular advantages in the area of human-machine interaction. This includes controlling work steps by voice command or gesture. This simplifies the collaboration flow and also enables collaboration in any language and with disabilities. In addition, for many work steps, it would be possible to automatically recognize when the human work step has been completed so that the cobot can carry out the next step. As a result, no communication is necessary. Another advantage is the emergency stop by voice, which can be implemented very robustly using embedded AI, even in noisy environments.

Effective and transparent production with AGVs

Between 2018 and 2020, the number of AGV installations increased by more than 100% - from 52,000 to 114,000. These "little" helpers can optimize the flow of materials and thus make conventional forklifts and warehouse ants obsolete, among other things. AGVs are currently used primarily in logistics, for example for unloading trucks and transporting goods from A to B. This allows logistics and industrial production to be controlled flexibly and transparently. AGV solutions are of interest to all companies that want to gradually automate and streamline their processes, which is also linked to their high scalability. Their importance will continue to grow in the coming years, influenced by variant diversity, smaller batch sizes and increasing quality requirements.

Loading and obstacle detection and maneuvering around people or objects are particularly important for driverless transport systems. Imaging and other perception methods can be used here, which are made even more robust by AI. Lidar (time-of-flight sensors) or radar are now so inexpensive that they can either be fused or work individually in three dimensions and robustly without any lighting requirements. In addition, new user interaction possibilities with people are opening up, as with the cobot, which is also beneficial for safety.

Increasing production efficiency with embedded AI

Embedded AI helps to make robots, cobots and AGVs fail-safe. In contrast to the previous cloud and edge solutions, embedded AI collects data on site at the application and does not have to send it to the cloud for processing. It enables much deeper data evaluation directly on site in the device, does not have to transfer large amounts of data and thus makes the failure of a component predictable.

Embedded AI is not just limited to maintenance, it is also important to integrate the technology more closely into production use - especially with regard to the increasing use of cobots and AGVs. If this integration and optimization is missed by innovations in Germany and Europe, it will be more difficult to keep up with highly automated production in Asian countries.