RWTH Contributes to Research on Off-Highway Digital Twins
Three RWTH institutes have joined forces to conduct the “Off-Highway Twins” research project, which investigates how and to what extent raw sensor data from off-road vehicles can be used to generate relevant geospatial information and to update geodata sets in cloud databases. The project receives about 100,000 euros in funding from the German Federal Ministry of Transport and Digital Infrastructure.
To facilitate the use of off-highway vehicles on construction sites, in forestry, or in agriculture, federal and state agencies provide a variety of relevant geospatial and other data. Often, these data are no longer up to date, the resolution of available data varies, and aerial photographs and terrain models, for example, are outdated or inaccurate. Off-highway vehicles equipped with sensors, however, would be able to collect data continuously and make them available for analysis.
The objective of the project is to investigate to what extent and using what methods the sensor data collected by off-road vehicles such as wheel loaders, mobile excavators, or harvesters can be evaluated in order to synthesize relevant information and make it available in cloud databases.
For example, vehicle motion and drive data could be used to determine how much material the vehicle has moved from one location to another and to modify the terrain model if a new hole or pile of soil has been created. Similarly, a machine can report to the cloud that a path is difficult to pass, even for high-powered vehicles, so that smaller machines can be directed to alternative paths.
If the collection of raw data from the mobile machines and the subsequent extraction of information turns out to be promising, the next step will be to determine whether information gaps in the available open-source cloud databases can be closed with the help of these data.
As a result, the execution and planning of construction processes could be improved and made more cost-efficient. Furthermore, the availability of high-resolution geolocation data would provide a basis for the automation of construction processes.
The project is jointly conducted by the Institute for Fluid Power Drives and Systems (ifas), the Institute for Human-Machine Interaction (MMI), and the Institute for Machine Elements and Systems Engineering (MSE).