IUAS – Institute for Unmanned Aerial Systems

The Hochschule Offenburg is a member of the joint project Programmable Systems for Intelligence and Automobiles (PRYSTINE), which is funded by the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL) and the German Federal Ministry of Education and Research (BMBF).

Within the PRYSTINE project, a fault-tolerant 360° all-round perception for highly automated driving is being developed, which is based on robust radar and lidar sensor fusion.

In the sub-project "Design of the system architecture of radar sensors based on identified scenarios", Offenburg University of Applied Sciences is involved in the specification and design of a system architecture for a novel RF-CMOS-based radar chip for the 76 - 81 GHz frequency range. The realization will be followed by various investigations for the validation of the radar system. The goal is to develop a future-proof CMOS-based radar system that is characterized by high robustness and high fault tolerance.

https://prystine.eu/

https://www.ecsel.eu/projects/prystine

Link to the report in the university magazine “Forschung im Fokus” (PDF)

Reference sensors for high-precision sensor validation for automated driving (RepliCar)

Reliable and precise perception of the environment is essential for autonomous driving. Sensors and the corresponding processing chains must meet the highest standards of accuracy and realism. However, there is currently a lack of approaches for effective and efficient validation of these systems. This is precisely where the RepliCar project comes in: The goal of the project is to build a reference system with a combination of high-resolution radar, camera, lidar, GNSS and inertial sensor technology, which will be integrated into a test vehicle.

This reference system will be several years ahead of today's standard sensor systems in terms of technology. Thanks to the integration of particularly high-resolution sensors and a powerful sensor data fusion for object recognition, a highly precise representation of reality, the so-called ‘ground truth’, will be possible - an important basis for the validation of sensors in the field of autonomous driving. IUAS is involved in the development of a high-resolution radar sensor with 48 transmitters and 64 receivers.

The central tasks of the IUAS include the design of the antenna array, the implementation of signal processing and the vehicle-compatible integration of the radar system.

The project runs from 1 July 2023 to 30 June 2026 and is funded by the Federal Ministry for Economic Affairs and Climate Protection.

The EdgeAI-Trust project aims to develop a domain-independent architecture for decentralized edge AI, along with HW/SW edge AI solutions and tools that enable fully collaborative AI. The Edge-AI technologies address the key challenges faced by European industry and society, such as trustworthiness, energy efficiency, system complexity and sustainability. The results of this project will be implemented in three target areas: Automated vehicles, manufacturing and agriculture.

Sensor-based AI is a key component of EdgeAI-Trust, which will aid the collaborative EdgeAI ecosystem. Within the EdgeAI-trust, the University of Applied Sciences Offenburg (HSO) is involved in the field of radar-based road condition detection and classification for autonomous driving, for which AI-based methods are to be used. The innovation of this task lies in a reliable classification method of the road surfaces using radars and AI which contributes to safe driving.

In EdgeAI-Trust,  HSO will make a significant contribution to the development of a sustainable and trustworthy AI-based solution for the road surface detection and classification. Radar sensors will be investigated for this purpose in the static and dynamic scenarios.

The development of the AI methods for the detection and classification of road surfaces will be based on the acquired measurement and simulation data.  In addition to analyzing the optimum mounting position of the radar on the vehicle, a ground penetrating radar (GPR) system is also implemented to obtain further information about the subsurface properties of the road.

The project is funded by the Key Digital Technologies Joint Undertaking (KDT-JU) and the Federal Ministry of Education and Research (BMBF).  The consortium consists of 51 partners from industry (OEMs and semiconductor suppliers) and research institutes.

Further details on the “EdgeAI-Trust" project can be found at: www.edgeai-trust.eu

During the industrial production of food, foreign bodies such as glass splinters, stones, plastics, ceramics or metal parts are occasionally added. Meat or fish products often still contain parts of bones or bones, fruit products (e.g. jam) seeds, stones or pieces of wood. Such foreign bodies can harm and injure consumers and must therefore be detected in good time at the end of the production line.

Nowadays, foreign bodies in food are detected with X-ray scanners. The application of this technique is complex, expensive and requires special radiation protection. For this reason, work is being done on alternative methods that can replace the X-ray method in special applications. This can be achieved with low-energy electromagnetic waves in the GHz and THz frequency range.

At the IUAS Institute, a measuring system is being developed that uses radar technology to detect foreign bodies in foodstuffs in the lower GHz range. The system takes advantage of the fact that foreign bodies reflect electromagnetic radiation in a characteristic way. The FMCW radar is a broadband MIMO system. The speed of foreign object detection is a particular challenge, since the available time is in the range of one second per target.

The industry has high expectations for radar technology. For this reason, it has been additionally supporting the development with a considerable sum for a few years.

Skin cancer is the most frequently diagnosed type of cancer in Germany. Every year, more than 200,000 new cases are diagnosed in Germany alone. According to WHO estimates, there are two to three million new cases worldwide every year. The most dangerous type of skin cancer is black skin cancer or malignant melanoma with approximately 20,000 cases per year in Germany and several thousand deaths.

The diagnosis of skin cancer is still based on visual techniques, where the detection rate can vary between 56% and 80%, depending on the experience of the diagnostician. Millimeter waves are a promising way to improve the accuracy of the diagnosis. For this purpose, the fact is used that the water content of skin cancer is different from healthy skin, which in turn is reflected in a change in the reflection of electromagnetic waves.

Together with the Karlsruhe University of Applied Sciences, skin cancer detection with millimeter waves is being researched.

Model predictive control (MPC) is one of the most successful advanced modern control methods, which can achieve very high control performance, especially in the context of nonlinear operating states, through continuous real-time optimization of the control inputs. Due to increasingly faster CPUs, the method can also be used in the sub-millisecond range, i.e. in particular also for mechatronic systems.

One investigated field of application is the use of MPC to optimize the navigation of renamed flight systems, such as the control of drones of airborne wind turbines.
For this purpose, MPC computes setpoints for the underlying control loops of the basic control system (altitude, airspeed, and attitude control).

Information about airborne wind turbines:

An airborne wind energy system (AWES) converts wind energy into useful electrical energy using an unmanned fixed-wing aircraft attached to a tether but flying freely. The motivation in developing an airborne wind energy system is to minimize the material required to build a conventional wind turbine. Thus, in general, the construction material of a wind energy converting turbine is always most efficiently used where the material is moving fastest relative to the wind. For example, in a conventional wind turbine, 70% of the energy conversion occurs at the outermost 30% of the rotor blades. The basic idea of an airborne wind turbine is to implement only this fast-moving part, i.e., a blade that moves quickly across the wind and is connected to the ground by a tether. If the tether is connected to a generator winch located on the ground and periodically retracted and extended, electrical power can be generated (illustration: see Figure 1).

In this case, the rope is unwound from the winch by the high aerodynamic forces applied to the wing, thus generating useful electrical power in the generator (unwinding mode). In retraction mode, the flight system is steered to a trajectory of reduced rope force and the rope is retracted with the generator acting as a motor, consuming energy. Net electrical energy is generated by periodic unwinding and rewinding.

Complex control systems, such as autonomous flight systems, usually have a distributed hardware architecture in which the command variable generator, the controllers and the sensors are implemented by separate components, which may be networked with each other via communication channels that are not hard real-time capable, such as wireless links. Due to a failure of the communication link, data can only be transmitted with a delay or even be lost completely. One issue under investigation is how these effects can be minimized by co-designing the control algorithm and communication protocol.

In the field of control of networked systems, control methods are being developed that are optimized for use in distributed systems.