Synthetic Data for Autonomous Construction Machinery

AI FOR THE GENERATION OF SYNTHETIC DATA FOR THE DEVELOPMENT AND TEST OF AUTONONOMOUS CONSTRUCTION MACHINERY

Autonomous construction machines are highly relevant to the automation of the computational design and construction. Future construction sites will require autonomous systems such as cranes, assembly robots, excavators, wheel loaders, etc. due to labour shortages, efficiency requirements or hazardous environments. Issues of development and testing of automatic functions become very relevant for the use of autonomous machines based on AI.

However, in this area of computational design and construction, it is difficult to generate real data sets for the complex scenarios of autonomous construction machines in order to develop and test the functionality. Synthetic data is required to create a sufficient number of scenarios, especially multiple test cases for the training of an AI and its functionality.

For example, in the development of autonomous construction machinery, typical scenarios include obstacles on the road, outdoors or indoors, workers surrounding the machine, or yielding ground. Rare events, which often represent critical situations, are hard to find in real data due to the limited operating time of prototypes and safety measures in operation. Generating of a large number of critical and rare situations in the real environment of an autonomous construction machine is practically not feasible due to high costs and safety risks.

This project will generate synthetic data by simulating rare scenarios in a co-simulation system consisting of an environmental simulation and a construction machine simulation, resulting in training, development and system test data sets.

PARTICIPATING RESEARCHER

Prof. Dr.-Ing. Dr. h.c. Weyrich
Institute of Industrial Automation and Software Engineering (IAS), University of Stuttgart

RESEARCHERS

Andreas Löcklin
Alexander Schuster
Iman Sonji

PARTNERS

Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB)
Robo-Test, Technologie-Transfer-Initiative GmbH an der Universität Stuttgart
Ingenieurgesellschaft Prof. Czurda und Partner mbH (ICP)

FUNDING

BMBF, grant number 01IS21057A