The task

The customer is developing an autonomously driving robot that is to be equipped with artificial intelligence for optimal navigation. With the help of deep learning algorithms, the first step is to identify different types of subsurface (e.g. concrete, grass, gravel, earth, etc.). The robot prototype has ten different sensors that collect image and time series data.

The challenge

The energy consumption of the algorithms should be dimensioned for the largest possible movement radius of the robot, at the same time a high, practical detection rate must be ensured.

our solution

The first step was to provide the robot's sensor data for the training of algorithms. For this purpose, we have provided workflows for loading, extracting and analyzing the data in a big data environment. In this environment, we then trained a selection of the latest deep learning models for the detection of several underground classes from image and time series data. Then the best models were optimized for resource consumption and achieved high accuracy with the lowest possible energy consumption.

The customer benefit

The resource-saving deep learning models enable use directly on the robot, which accelerates development cycles and significantly more development ideas can be tested in a shorter time. The close cooperation enables the customer to train further models in self-service and to use them on the prototype.