Copyright: © ifas

Existing geodata from different terrains, such as construction sites or agriculture, are of utmost relevance for the operation of Off-Highway-Twins. In the context of the "Off-Highway-Twins" project, a feasibility study is being conducted in joint cooperation between ifas, the Institute for Human-Machine Interaction and the Institute for Machine Elements and System Development. The project investigates to what extent and with which methods raw sensor data from mobile machines are suitable to synthesize further information from them and to update geodata sets in cloud databases.

Benefit Procedure

Utilization of the collected sensor measurement data

Retrofitting the excavator with sensor technology

Derivation of a digital twin from existing data

Creation of a Digital Twin

Zwischenschritt zur automatisierten Datenbankaktualisierung

Testing on a test site of the RWTH



Mobile Machines




Mobile machines are increasingly generating large amounts of data in their various application scenarios. This data usually remains unused and its potential is thus wasted. As part of the BMDV-funded study "Off-Highway Twins", a network of research institutes at RWTH Aachen University and the Center for Mobile Machinery have jointly investigated what data is generated in the everyday work of mobile machinery and how information can be derived from it on an ongoing basis. The research institutes involved in this project, the Institute for Fluid Power Drives and Systems (ifas), the Institute for Human-Machine Interaction (MMI) and the Institute for Machine Elements and System Development (MSE), dealt with this question as part of the research project "Off-Highway-Twins" (Mach1nUp2Date). Specifically, the project was concerned with the extent to which and the methods with which sensor data collected from mobile machines can be suitably evaluated in order to synthesise further information from it and make it available in cloud databases.


Background situation in Germany

Today, federal and state agencies provide a variety of different (geospatial) data that is extremely relevant to off-highway vehicles on construction sites, in forests, in agriculture, and in other areas. Looking at the timeliness of this data, one finds that it varies. In North Rhine-Westphalia, aerial photographs are up to three years old and terrain models up to six years old. The resolution of these data is usually 20 cm/pixel for aerial photographs and 0.5 m for terrain models. In addition, aerial imagery often suffers from positional inaccuracies due to parallax effects and other data sources, such as the ground map, are often not sufficiently resolved. These facts more often lead to information gaps when using the data.


Mobile machines as sensor

Mobile off-highway vehicles, such as wheel loaders or mobile excavators, are equipped with various sensors that have been installed by the manufacturer for machine operation. By means of this sensor technology, data is continuously collected during operation in the various working environments. Many mobile off-highway machines operate in environments for which few detailed data sets are immediately available. Here, deeper analysis of the machine's sensor data offers the potential to close this gap. Today, however, the data collected by sensors is rarely analyzed to provide further information about the environment and then made available to the general public to augment existing data sources.


Determining the potential of collected and processed sensor data

The project dealt with the extent to which the data collected by various sensors can be processed and further information obtained. For this purpose, a mobile working machine was equipped with modern sensor technology, which is used to determine environmental characteristics and soil conditions. For the study carried out, extensive test drives were carried out in different environmental scenarios in order to compare corresponding data characteristics with each other. The subsequent data processing showed, among other things, that with the help of indirect measurement data, such as the pressure of a cylinder, the condition of the paths can be determined in the same way as the deflection of an acceleration sensor. In addition to the topographical condition of the ground, the slip of the wheels can also be used to make a statement about the grip of the ground. Furthermore, it was possible to determine the quantity of graves. With the help of laser scanners, the surroundings and vegetation could also be depicted in detail. The features mentioned and many more could be assigned to a unique position by GPS receivers, which made it possible to describe the test environment in detail and to record the progressive change in the working environment. The findings were presented to a broad consortium at a workshop at the end of the project.


Application possibilities and outlook

The research carried out shows great potential in the collection of raw data from a mobile vehicle and its information extraction. Further research potential arises with regard to closing information gaps on the various open source cloud databases. This facilitates and improves the implementation and planning of construction processes and can thus lead to a reduction in the costs of construction processes. Furthermore, detailed geodata offer a cornerstone for the realisation of autonomous construction processes.



The project was funded by the Federal Ministry of Digital Affairs and Transport (BMDV). Special thanks also go to the project partners the Institute for Human-Machine Interaction (MMI) and the Institute for Machine Elements and Machine Design (MSE) for their successful cooperation.

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