With the ever-increasing volume of transported goods worldwide an enhanced efficiency in logistics processes is getting more and more important. Typically, this results in a higher level of automation for inter- and intra-logistics. The chair for material flow and warehousing at the TU Dortmund University hosts an intra-logistics campus as a testbed for evaluating automatization solutions. At this testbed material flows via autonomous systems can be emulated and optimized via Machine Learning. As part of the Summer School, a hackathon provides you with access to positioning data, application of machine learning to this data and ultimately controlling a robot based on your predictions.

Machine Learning Task

For controlling robots in a warehouse scenario, knowledge of their positions is necessary for centralized control of material flows. While commercial solutions for positioning like camera or beacon systems are available of the shelf, these solutions are expensive, need complicated setups and may not even be able to cover the whole warehouse, e.g. during shadowing from racks and shelves. The experimental lab at TU Dortmund University features a unique sensor flow, consisting of a regular grid of sensor nodes, i.a. with vibration and magnetometer sensors. The time series of these sensor measurements will be provided to the Summer School participants, each measurement accompanied by the exact position of the robot provided via a commercial positioning system as the label.

You may use whatever Machine Learning algorithm and language you prefer for training a model predicting the x/y position in the local coordinate systems. Teams with the best models will get access to the real robot transportation system and solely control the robots based on the sensor measurements and their prediction.

Link to the Kaggle challenge coming soon...

Live Event - Control your robots

On friday, 4th of September, the teams with the best model's prediction for the robot’s position will get the chance to use these predictions for controlling the robot in the live environment. We will provide you with a simple REST API for querying recent sensor floor data and commanding the robot via pushing directional request.

You will have to guide the robot through an obstacle course in minimum time with minimum errors.

The final winning team will get an invitation to TU Dortmund University (travel costs covered) to visit the logistics lab (as soon as possible again), present their approach at the solution and the chance to jointly write a publication about the whole process with the competition's researchers.