Off-Highway-Twins

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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

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Off-Highway-Twins

The objective of the project is to investigate the potential of collected and processed sensor data. Specifically, the question is to what extent and with which methods the sensor data collected by mobile working machines can be suitably evaluated in order to synthesize further information from them and make it available in cloud databases. In the project, an off-highway vehicle will therefore be equipped with appropriate sensor technology and measurement infrastructure and put into operation for data collection at a test site of the RWTH. Furthermore, a digital twin (DZ) will be created based on existing data and the feasibility of updating this DZ with the collected sensor data will be tested.

 

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.

 

Investigation of the potential of machine telemetry

The project deals with the extent to which the data collected by various sensors can be processed and further information obtained. Thereby, the data acquisition is considered in several aspects, such as scope, detail and accuracy of the sensor data. Furthermore, different methods for the synthesis of semantic information from the collected raw sensor data will be tested. For example, a machine's motion and drive data can be used to determine how much material it moved from one location to another and then note in the terrain model where a new hole and where a pile of soil occurred and their extent. Similarly, a machine can report to the cloud that a path is difficult to pass due to adverse conditions, even with strong drive power, so that smaller machines can directly choose an alternative path. The conceivable scenarios, the possible uses and the added value of such information are manifold and a key to increasing productivity for various industries.

 

Application possibilities and outlook

If the investigation of the collection of raw data from a mobile vehicle and its information extraction shows potential, the next step is to determine whether the information gaps on the various open-source cloud databases can be filled with them. Further, publicly available cloud databases allow all authenticated contributors to access the data and share their collected and processed data with others. This facilitates and improves the execution and planning of construction processes and can thus lead to cost reductions in construction processes. Furthermore, detailed geodata offer a cornerstone for the realization of autonomous construction processes.

 

Acknowledgement

The project is funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI).

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