Projects

Drone-based object monitoring enables the identification of damage, e.g. to bridges or wind turbines. However, quantitative measurements, such as determining the depth of damage to cracks or dents, are technically challenging, particularly due to disruptive environmental influences such as wind movements and vibrations of the object being measured. To solve this problem, an innovative method is being developed that can directly measure the relative disturbing movements between the drone and the object. This is done using a fibre-optic sensor, which temporarily establishes direct contact between the drone and the object being measured. The information obtained can then be used to correct the actual measurement data and is to be used in this project, for example, in the 3D image stabilisation of a close-range lidar.

In this project, a drone-supported measuring system is being developed for the digitalisation of infrastructure or large-scale industrial products. The measuring system attached to the drone consists of kinematics for stabilisation and a fringe light projection system for surface measurement in the sub-millimetre range. The localisation and thus combination of the individual measurements is made possible by a laser tracking system that can be moved by mobile robots and considerably expands the working area.

In the project, metasurfaces (reconfigurable intelligent surfaces) are combined with drones in such a way that radar-based position measurements around obstacles become possible. The project aims to model the measurement accuracy and then improve it based on models and optimisation.

We have developed a multicopter that can follow flying insects in nature. We measure the 3D georeferenced position of bees and other insects over hundreds or thousands of metres. In this project, we quantify and improve the performance of these position measurements.

During rescue operations after building collapses, rescue teams often do not know where people are buried or where there is a risk of gas leaks.  We enable safer rescue operations with drone-based, innovative measurement technology for natural gas leaks and bioradar. The aim is to systematically determine the measurement quality of both systems depending on the resources of time, size and energy (‘measurement uncertainty budget’).

In the context of heat and drought extremes, the leaf water content of plants plays a central role as a stress indicator. With the aim of recording the leaf water content in 3D and at any time of day, the project is developing a dual-wavelength LiDAR with tuned absorption bands. Based on a process chain to be developed, the leaf water content and corresponding uncertainties are automatically derived.

The aim of this project is to quantitatively measure the moisture content of the soil using radar sensors. The sensors are attached to a drone. A new measurement principle with a corresponding sensor and a new signal processing concept are to be researched for this measurement task.

In this project, a drone-mounted sensor system for the real-time measurement of nitrite and nitrate levels in open waters is being realised, which enables the mapping of nutrient concentrations. To this end, an automated chip laboratory developed in preliminary work is being further developed for drone assembly. The project focusses on the investigation of measurement accuracy as a function of resources such as weight, energy and time per measurement.

The aim of the project FliMoGaTo is to enable laser absorption measurements between two flying drones to measure gas and trace substances in the air without the drones' rotors disturbing the gas distribution. To this end, gas distribution models are created from remote measurements (utilizing domain knowledge in the form of physical differential equations), and, on this basis, intelligent measurement plans are calculated. Our aim is to create spatial maps of the gas distribution as accurately and as efficiently as possible.

The project description for D4 will follow soon.

This project concentrates on the development of a design strategy including the hardware implementation of a drone-compatible, dual ion mobility spectrometer (IMS) with non-radioactive ionization source, high resolving power and detection limits in the ppt range with measurement times of less than one second for rapid analysis of air samples in safety and security applications as well as in environmental measurement applications. A drone-compatible gas chromatograph (GC) with IMS as a detector will be also developed for quantitative analysis of complex samples. Rapid remote sensing of a larger area is then carried out by UAV-IMS, while the GC can be used on demand by remotely coupling the GC to the IMS as required for quantitative analyses of sensitive areas by UAV-GC-IMS.