Research Projects
Since its foundation, FLW has conducted research in the field of warehousing logistics. In collaboration with other departments at TU Dortmund University, we have continuously supported logistics education and research. Joint research projects are carried out in close cooperation with Fraunhofer Institute for Material Flow and Logistics IML. Additional interdisciplinary collaborations take place with other institutions at TU Dortmund University, with national and international universities, and with industrial partners as part of larger projects. As a founding member and long-time board member of the Scientific Society for Technical Logistics (WGTL), Prof. Michael ten Hompel has played a key role, both through his professorship and within the society, in advancing technical logistics as a young, interdisciplinary discipline in both the academic and industrial communities.
Current Research Projects
"Open – Efficient – Secure – Safe" – Research 6G mobile communications systems
The Chair of Conveying and Storage Systems develops modern technologies for cyber-physical intralogistics systems and conducts research into mobile communications and broadband technologies. Future 6G mobile communications systems should not only be network operator-centric, but also offer innovative standard architectures for a wide range of applications.
LAMARR Institute of Machine Learning and Artificial Intelligence strives to create the future of European AI. Constituted by the Technical University Dortmund, University Bonn, Fraunhofer IAIS, and Fraunhofer IML, researchers at LAMARR focus on the research and development of high-performing, trustworthy, and sustainable AI solutions for hands-on business applications. The critical research interest areas are Hybrid Machine Learning, Resource-aware Machine Learning, Trustworthy Artificial Intelligence, Embodied Artificial Intelligence, and Human-centred Systems, focusing on applications such as Planning and Logistics, Astrophysics, Industry and Production, Life Sciences, and Natural Language Processing.
The Chair of Conveying and Storage Systems is collaborating with industry and research partners on a project to improve the efficiency and ergonomics of manual work processes in logistics. MotionMiners technology is used to analyze real movement data from warehouses using AI to create high-quality simulation models. The results of the project are expected to significantly optimize manual work processes.
The funded project Machine 2 X, or M2X for short, is developing interfaces for communication in the field of driverless transport systems (AGVs). These include interfaces for order placement and interaction with peripheral devices. The project is being managed by the Chair of Conveyors and Storage Systems at the Technical University of Dortmund. The Fraunhofer IML is an associated partner. The project is supported by VDMA e. V. and over 20 other partners from industry. Current project content and further announcements are posted here on the LinkedIn page. Development is community-driven via GitHub. The latest news can be found on the project's LinkedIn page.
The regions of Dortmund, Unna, and Hamm (DUH) want to establish a hydrogen region to facilitate the phase-out of coal. Blockchain technology is to be used to improve cooperation between companies. The DUH-IT project helps small and medium-sized enterprises (SMEs) to make better use of blockchain technology.
The “Scalable AI and blockchain solution for automation and autonomization in value creation networks” (SKALA) project aims to optimize cross-company data exchange in value creation networks through the use of blockchain and artificial intelligence (AI). The focus here is on the automation and autonomization of processes.
The „EventRec“ project aims to enable high-speed AGVs (>10 m/s) to perceive indoor logistics environments accurately, addressing the limitations of current AGVs that prioritize precision over speed. By integrating advanced perception systems, these AGVs can recognize static and dynamic objects for efficient and reliable warehouse operations, reducing the need for slowdowns during tasks like object handling. Simulations in a VR lab will use Motion Capture, stereo event cameras, and RGB cameras, alongside Deep Learning and computer vision algorithms developed with C++, ROS, and Python.
Finalized Research Projects
Logistical design principles are intended to provide astronauts with more space and time to focus on their space research.
The research project being conducted by TU Dortmund University in collaboration with Fraunhofer IML has the overarching goal of transferring established principles of terrestrial logistics to the field of space travel in order to meet the increasing demands that space travel places on logistics – for example, longer travel distances, longer stays, and increasing space tourism mean that more goods have to be transported and stored.
The intelligent container - With the “inBin”, the Fraunhofer IML and the Chair of Materials Handling and Warehousing at TU Dortmund University present another decisive step on the way to the Internet of Things. The first truly intelligent container communicates with people and machines, makes independent decisions, monitors its environmental conditions and controls logistics processes. This transforms the load carrier into a “co-thinker”. Internal project
Application of innovative methods and solution processes from the field of artificial intelligence in intralogistics. As part of DFG Research Group 1513, methods from the field of artificial intelligence are being further developed to enable and improve the processing of both qualitative and quantitative information. One aspect of the research is the further development of solvers and paradigms from the field of response quantity programming. On the one hand, intralogistics, with its diverse problems and challenges, offers an ideal platform for validating the developed results using practical application cases. On the other hand, it offers the opportunity to reveal potential benefits in intralogistics through the application of innovative methods from the field of computer science. Specifically, response quantity programming is being applied in the field of autonomous vehicles and to support the planning of distribution centers. The research is being conducted in close cooperation with the “Information Engineering” working group of Chair 1 – Logic in Computer Science at TU Dortmund University.
The Innovation Lab strengthens Dortmund's pioneering role in the field of digital services and logistics. It accelerates the acceptance and introduction of new technical solutions in the context of Industry 4.0. Development processes in technology and organization, as well as the social challenges of hybrid services, are analyzed from a sociological perspective and alternative design paths (including work organization and qualification structures) and human-centered solutions are identified. The path to market is shortened. The future viability and competitiveness of companies increases and jobs are secured in Germany.
The PAL2REC project aims to identify movement patterns by evaluating and interpreting sensor data from load carriers. The movements of sensor-equipped load carriers (e.g., pallets) are to be traced back to logistical processes (such as forklift handling) using only sensor data, without including other necessary accompanying information such as camera images outside the load carrier. The project goal of FLW and its project partners is to demonstrate the feasibility of reproducible recognition of logistics processes.
Funding Reference Number: 19F1174B
The integration of drones into logistics and warehousing has been gaining increasing interest. However, drones are not yet effectively utilized alongside or in interaction with humans within industries. One contributing factor, aside from the need for safety regulations, is the lack of experiences and knowledge about how humans trust, distrust or over trust drones in the workplace. There is a pressing need to comprehensively understand the process of trust calibration and to identify the key factors essential for developing calibrated trust between humans and drones.
Project Goal: The goal of the project is to conceptualize trust in human-machine interactions and lay foundations for “Theory of Artificial Mind (ToAM)” within the context of everyday work scenarios. This project aims to understand the process of trust calibration over time and the elements that the ToAM should encompass, considering the perspectives of consumers, workers and intelligent systems.
For example, envision a newly hired worker in a logistics centre. At the entrance, a drone greets the worker. The drone provides guidance and shows him/her specific aisles and shelfs. Throughout this process, the drone must navigate without collisions and anticipate the movements of other objects or persons in motion.
Implementation: In a specially designed indoor logistics scenario, 20 participants take on the role of the worker, who is welcomed by the drone and guided through a number of aisle and shelfs. While being guided by the drones, several calibration points arise, e.g. the drone flies around a bend in the path and is out of the worker’s sight. Theses intentionally designed calibration points are used for assessing the mental model of the participants and its adjustments over time. Additionally, a digital twin will be generated to facilitate the simulation and monitoring of the scenarios.
Funded by: The project is funded as “Incubator project” by the Research Center Trustworthy Data Science and Security, one of four research centers within the UA Ruhr that are funded by the Ministry of Culture and Science of the State of North Rhine-Westphalia.
Project Partners: Chair AOW at Ruhr Universität Bochum, Mathematical Statistics and Applications in Industry at TU Dortmund, Chair of Material Handling and Warehousing at TU Dortmund.
Contact Person: M Sc. Shrutarv Awasthi
The Chair of Conveying and Storage Systems is assisting the Information Engineering working group at TU Dortmund University with the DFG project CASPER. The aim is to develop an app that models knowledge in academic logistics to support creative tasks such as warehouse planning. The challenges in logistics can also be applied to other areas.
The Silicon Economy is synonymous with an upcoming digital infrastructure or digital ecosystem that is based on the automated negotiation, scheduling and control of goods flows and enables new, digital business models (not only) for logistics. This digital infrastructure enables comprehensive transparency in value creation networks and creates trust along entire supply chains, from the raw material supplier to the end customer - perhaps the most important prerequisite for the participation of all companies.
In logistics, the field of application consists of a large number of different players who often do not share a common basis of trust. Blockchain technology has a key role to play in this context due to its potential to ensure tamper-proof data exchange and to automate and, in future, autonomize numerous value-adding processes. This is why we are participating in Blockchain Europe - the project to establish the European Blockchain Institute in NRW. For example, in cooperation with the other project participants, we are developing a demonstrator at our research center that shows the application potential of blockchain technology in the field of production and production preparation.
DFG-Project “Transfer Learning for Human Activity Recognition in Logistics” - Knowledge of the duration of manual activities plays an important role in the planning and optimization of order picking systems. The aim of the research project is to develop a method for analyzing the manual activities of an order picking process, with which, among other things, the picking time can be automatically recorded.
This method uses sensor-based activity recognition. On the one hand, the order picker is equipped with mobile sensors that record various physical parameters such as acceleration. On the other hand, human movement during order picking is recorded with millimeter precision using the motion capturing system in the FLW research hall. This creates a reference system to validate the sensor data. The activities relevant to order picking are identified and automatically recognizable in the sensor data using pattern recognition methods. Individual sub-processes associated with order picking, e.g. order processing or replenishment, are determined from the recognized movements. For practical applicability in different scenarios and systems, possibilities for semi-automatic annotation of the sensor data are being investigated with the aid of reference data from the motion capturing system. The project is being carried out in cooperation with LS XII - Pattern Recognition in Embedded Systems Group of the Faculty of Computer Science (TU Dortmund University).
Research into artificial intelligence (AI) is having an impact on society as a whole, changing productivity and living standards. Data is the new raw material. It is driving this development. The computers already available are powerful enough to analyze the ever-increasing amounts of data, detect correlations, learn new things and use them. Models for solving problems are calculated and further developed independently in a reasonable amount of time. - The transfer of these processes to robotics in industrial applications offers enormous potential for Germany as a business location. However, specialists are needed who combine the specialist knowledge of their own domain with AI expertise. The demand for such employees significantly exceeds Germany's current training and further education capacities, meaning that the enormous potential gained can only be partially exploited. At the same time, research into the application of machine learning in robotics requires scenarios with autonomously controllable and authentic hardware suitable for industry in order to be able to supplement and expand on results achieved via simulation that do not have sufficient accuracy for direct transfer. With the right infrastructure, scientists in the AI Arena will be able to meet the high research requirements of AI in application with industry-oriented hardware.
The aim of the Competence Center Machine Learning Rhine-Ruhr is to bring machine learning technologies in Germany to a globally leading level. As one of six nationwide hubs for artificial intelligence (AI) and machine learning (ML), the ML2R center is funded by the Federal Ministry of Education and Research (BMBF). The Technical University of Dortmund, the Fraunhofer Institutes for Intelligent Analysis and Information Systems IAIS in Sankt Augustin and for Material Flow and Logistics IML in Dortmund and the University of Bonn are involved.
Knowledge of the duration of manual activities plays an important role in the operation and optimization of picking systems, as well as in their redesign. The aim of the research project proposed here is to develop a method for analyzing the manual picking process, which can be used, among other things, to automatically record the proportion of time spent on picking. This method uses sensor-based activity recognition. For this purpose, the picker is equipped with mobile sensors that record various physical variables such as acceleration during picking. This data is then evaluated, condensed, and prepared for process analysis. To do this, movements relevant to picking are first identified and then processed using static pattern recognition methods. The recognized movements are then used to determine logistical activities, i.e., individual sub-processes associated with picking (e.g., processing orders or replenishing stock), which are an important object of consideration for the automatic analysis of picking. For practical applicability in different scenarios and systems, the possibility of (semi-)automatic adaptation of the developed method using machine learning methods will also be investigated. – Funded by: German Research Foundation (DFG)
The aim of the joint project between Fraunhofer, TU Dortmund University and the EffizienzCluster is to create a globally visible research and development center for logistics and IT in Dortmund with partners from industry and science. The result is the bundling of research and industrial expertise on a par with large international research centers. The aim of the center is to lay the theoretical and methodological foundations of logistics as a science. In addition to consolidating expertise in terms of content and science, the declared aim is to provide outstanding teaching and produce excellently trained young logistics professionals. The chair is involved in setting up the order picking laboratory, which will start work in 2021.
Companies must adapt their factory systems quickly and efficiently to the increasingly dynamic changes in their environment. One possible solution is the use of decentralized autonomous intelligence that organizes itself.
Logistics & supply chain strategies for modular chemical and pharmaceutical production Investigation of new opportunities and requirements for logistics when converting WorldScale production facilities to regional and mobile modules. – At FLW: Examination of the modularization of intralogistics processes and strategies, selection and development of usable logistics systems, creation of a concept for the control and arrangement or layout of sites for modular production. Funded by the 'Ziel2.NRW program' and the European Regional Development Fund: “Operational Program Regional Competitiveness and Employment 2007–2013 (EFRE)” Material flow planning, material flow control
With the two individual projects “C1 – Analytical Methods for Calculating the Performance Availability of Complex Material Flow Systems” and “C5 – Real-Time Logistics,” the FLW Chair is participating in the PAK672 cooperation project, which consists of five subprojects. The research focuses on determining and ensuring the performance availability of intralogistics systems at the level of (control engineering) system components. This should lay the foundation for designing efficient systems and arriving at a realistic assessment of internal material flows. At the level of performance evaluation of entire supply networks, the project results can be used to evaluate network nodes with intralogistics functions. The collaborative project thus contributes to meeting the requirements for greater flexibility and robustness in modern intralogistics systems.
The main objective of the research project is to analyze the problem areas of a lack of standardization and adaptability in the field of conveyor systems with the support of several participating companies and to enable the development of standardized, collaborative engineering processes. This will enable cross-company and end-to-end (re)planning and implementation of heterogeneous, decentralized intralogistics systems and ensure efficient operation. The focus is on the design, implementation, and documentation of an open collaboration platform that ensures a uniform database and enables virtual integration testing of all plant components involved. The design specifications for the interface implementation of heterogeneous components and subsystems as well as the coupling of emerging planning and simulation tools via a standard interface are the subject of in-depth consideration. In this way, the intralogistics systems can be tested jointly by several suppliers and put into operation virtually. This reduces the effort required for planning, implementation, and ramp-up. The IGF project 17391 N of the BVL research association was funded by the German Federal Ministry of Economics and Technology through the AiF within the framework of the program for the promotion of industrial joint research (IGF) based on a resolution of the German Bundestag.
Resource-efficient and distributed platforms for integrative data analysis - A test field for resource-limited, intelligent goods carriers is currently being set up. This test field is to be integrated into the existing 1,020m² test hall for cellular conveyor technology (ZFT) at the Fraunhofer IML, whose managing director is Mr. ten Hompel. The set-up will be carried out with the help of start-up funding from the new Chair of Materials Handling and Warehousing. 350 hardware platforms (some with displays) will be developed and manufactured, which will be placed as interactive test platforms in a realistic logistics context and enable the measurement and mapping of environmental conditions for energy harvesting. Tests can also be carried out on the scalability of wireless communication. All hardware nodes are controlled by software based on the concepts developed in SP A4. A special feature of this testbed is the ability to move sensor nodes around the room using the existing cellular conveyor technology. In contrast to existing wireless sensor network testbeds, the new testbed will investigate energy harvesting methods in a realistic, industrial indoor environment.