Autonomous

Multisensor

Systems

Summary
“IMIQ – Intelligent Mobility Space in the District” is a project of the IMR – Intelligent Mobility Space Saxony Anhalt (https://niimo.ovgu.de/en/Intelligent+Mobility+Space.html), which will be based in the Science Harbor in Magdeburg. Over a period of 3 1/2 years (01/2024 - 12/2027, actual operational start 8/2024), the science harbor will become a future district in which new solutions will be developed in a needs-oriented manner, tested technically and informationally and implemented socio-economically . Key innovations include a digital work-life twin (DMLZ) and a real-world laboratory for intelligent mobility (RIM).

Ambitions
The aim is to develop and test innovative mobility and communication approaches. A digital work-life twin (DWLZ) enables a holistic and innovative mobility and communication experience that offers efficient and personalized solutions through sensors, 5G and digital services and at the same time promotes social interaction and exchange on site. In the Intelligent Mobility Real Laboratory (RIM), the researchers' developments on intelligent mobility become physically visible and tangible/experienceable; they are tested and evaluated. Technologies for communication and V2X, localization and tracking are controlled in an operation control center, integrated with infrastructure (including mobility stations) and with implemented autonomous vehicles.

Further Information
You can find a detailed description, news and staff positions here: https://niimo.ovgu.de/IMIQ.html. Under this link, or under the names linked above, you will also find information about the IMIQ work areas of the project partners.

This project will build up cutting-edge research in the interdisciplinary research field of mobility at the OVGU and enable the transfer of new mobility solutions in Saxony-Anhalt and beyond. The visibility or experience is aimed at all stakeholders.

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The Modular Peristaltic Surface Conveyor (MPSC) is an entirely new device that conceptually enables the separation and sorting of flexible small packages (polybags) for the first time, providing an alternative to costly manual processing. For the first time, alongside the development of the actual MPSC, an AI-based Digital Twin (DT) is to be developed, which, based on AI-optimized simulation models, will allow predictions of system behavior and automated parameterization of the actuators and sensor data processing.

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SeaSentry aims to develop a land-based sensor network for passive detection and real-time localization of ship movements, which functions without requiring additional installations onboard. This technology enhances and improves existing monitoring systems, providing an innovative solution for maritime traffic control.

A key aspect of the project is the testing of the sensor technology in the eMIR testbed in the German Bight, which extends from the Elbe estuary to the port of Emden. This test area offers a variety of maritime scenarios to evaluate the technology under real conditions. To test applications with greater range, the eMIR testbed will be extended to the Heligoland site.

The developed technology will be integrated into VTS systems to improve efficiency and safety in maritime environments. The passively operating sensor network enables reliable detection of ships without the need for additional devices onboard. SeaSentry thus contributes to maritime safety and has the potential to revolutionize the monitoring of ship movements in complex environments.
At Otto von Guericke University Magdeburg, researchers are developing algorithms to localize and track ships using the SeaSentry sensor network. Initially, their focus is on enhancing signal processing techniques, particularly peak detection. The peak times identified at each sensor node will be converted into position estimates for individual ships. By incorporating motion models and dynamic estimation methods, a comprehensive tracking system will be created. This system will include track management, measurement assignments, and uncertainty assessments for each ship to ensure the required accuracy for the overall project. In the second phase, methods for optimizing sensor placement will be developed.

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Scientific goals
The idea of co-evolution at the human-technology interface is based on the fact that both the biological side and the technical side of an interface are not only dynamic and adaptive, but also take account of the other side in their adaptivity. Investigating this mutual influence leads to a deeper understanding of the causes of undesired processes, such as the maladaptation of inflammatory responses to unwanted changes in implant surfaces. This understanding then opens up new strategies to support desired processes in the sense of co-evolution. These include the possibilities of adaptive technologies and sensor approaches that can adjust to individual dynamics in the biological system, or the development of process-aware technologies that can bring about desired dynamics in the biological system.

Intended strategic goals
The modules of the TACTIC graduate school are designed to enhance translational expertise in the fields of medical technology, sensor technology, and artificial intelligence (AI). The goal is to strengthen research, development, and innovation activities on site. The aim is to closely interlink life sciences and engineering across all modules in order to facilitate future collaborative projects in this area. In addition, the integration of AI is intended to strengthen the profile area of medical technology. By internationalizing the research focus areas, TACTIC enables networking with EU partners, which is an important prerequisite for the alignment of consortia in order to strengthen science in Saxony-Anhalt.

Work program
The graduate school comprises three modules with a total of 9 doctoral students. A thematic network is established through doctoral topics, where at least two thematic modules are assigned concurrently. Each of the three thematic modules - Interaction, AI and Interface - is endowed with three doctoral positions (100%). The aim is to qualify our doctoral students for both the academic and private sector job markets. Interdisciplinary skills are to be imparted through doctoral seminars. Annual thesis committee meetings and TACTIC symposia support the development of doctoral students. An international network is to be established through presentations at international conferences and self-organized symposia.

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The amount of sensor data provided by battery-driven, widely distributed devices is steadily increasing. Since sensor data are typically fed into information processing units, it is worth considering how information processing itself can be exploited to reduce communication and energy demands. For this purpose, this project focuses on information-processing techniques that can incorporate implicit information conveyed by the transmission mechanism. Although a sensor node decides not to send its data, the receiver can still leverage the absence of data to update its state estimates. For instance, sensor readings can be compared against a threshold to decide for a transmission. The receiver can translate this decision rule into information about the data although no transmission took place. Sender and receiver can negotiate such decision rules in order to minimize communication costs, on the transmitting end, and to maximize the retrievable information, on the receiving end. Since threshold-based strategies are far too restrictive for time-varying systems being observed, model-based and data-driven policies will be investigated.

This project primarily investigates stochastic decision rules to trigger transmissions. In contrast to deterministic triggers, stochastic mechanisms can preserve the Gaussianity of the implicit information simplifying the estimator design at the receiver. For instance, a Kalman filter only requires minor adaptions to incorporate implicit information when no transmission event is triggered. The goal of this project is to push the principles of stochastic triggering forward to establish a comprehensive framework of lazy estimation. First, the investigations are concerned with general properties and the design of intelligent trigger decisions to improve the effectiveness and robustness of lazy state estimation. These include model-based and data-driven trigger mechanisms, aperiodic and asynchronous transmission and processing times, as well as the study of unreliable communication links. The results provide the foundations for large-scale lazy estimation with respect to both multisensor systems and high-dimensional state representations. For instance, multiple systems collaboratively monitor a dynamic system and fuse exchanged sensor data and estimates. Such distributed data fusion problems lead to dependent trigger decisions that require self-adapting trigger mechanisms. In particular, the project considers applications in object tracking to evaluate the derived concepts. Lazy estimation shows great potential in the processing of neuromorphic sensor data.

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As part of the DatAmount research project, methods are being developed that make it possible to create energy models of machine tools. These models are suitable for predicting the energetic behavior of machines for new products on the basis of small amounts of data. Since small series are often produced, especially in the SME context, in many cases, there is not enough data to train AI models. Physical modeling, on the other hand, is often very costly. Due to the required CO2 proofs and the climate targets set, companies thus find themselves in a field of tension. On the one hand, accurate models to predict the energy consumption of machines are necessary to remain competitive. On the other hand, the creation of such models is currently either very expensive or not possible. The current mostly manual prediction of energy consumption is also time-consuming and also person-bound. The approach presented here combines physical models of the energy behavior of machines with data-based machine learning models, with particularly data-efficient machine learning models being investigated. This enables an automated, accurate prediction of the energy consumption of machine tools. The benefit for SMEs is the efficient creation of models that can predict the energy consumption and CO2 emissions of new products. These predictions are often necessary to be considered in a tender as proof of energy and resource efficiency is often mandatory in bids from larger companies with CO2 reduction targets.

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Problem Definition
Autonomously operating mobility systems or delivery services open up considerable development potential with regard to quality of life and services of general interest in non-urban areas such as in the former brown coal regions of Germany. However, assessing the potential success of micromobiles operating autonomously on footpaths and cycle paths requires comprehensive environmental information from the areas of operation, such as minimum path widths, the volume of foot traffic or sight lines. In particular, outside of large cities, this information is incomplete and heterogeneously structured.

Project Objectives
The aim of the project is to design strategies for the bicycle-based collection of environmental data that are relevant for the successful operation of autonomous micromobiles on footpaths (visibility of certain areas, infrastructure parameters, passenger volume, network coverage, environmental data). For this purpose, the project evaluates different collection methods with regard to the efficiency and quality of the aggregated information. The usability of the data will be examined in two concrete smart city/town application scenarios (rental bicycles with autonomous delivery mode and delivery robots) with corresponding studies. In this way, the project contributes to the data-driven development of smart mobility and logistics concepts that cover the specific features of different settlement areas.

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