Principal investigators: Marco Caccamo (TUM), Raymond KNOPP (IMT), David Gesbert (IMT); Other contact person: Harald Bayerlein (TUM), Omid Esrafilian (IMT)
Short summary and central question:
As 5G networks are being rolled out, many studies and efforts have begun exploring the next generation of wireless networks (6G). It appears that 5G in its current standardized form will fall short of fulfilling all expectations in the face of extraordinary future challenges. The main principles of future mobile communication systems are the ability to handle a higher degree of flexibility and functionality which enables future intelligent connectivity and control along with joint communication and sensing; Moving away from traditional network architectures with passive terminal devices towards ultra-flexible and self-configurable network architectures with intelligent and connected devices. By capitalizing on the internode connectivity provided by networks, the intelligent nodes can also perform collaborative sensing and control.
Having an ultra-dynamic network becomes extremely important when it comes to fast responses in the face of crises triggered by natural or human-caused disasters such as earthquakes, fires, floods, wars, etc. In such scenarios, the aid of a connected dynamic network comprising intelligent nodes, such as UAVs and ground mobile robots, can save lives and help society resume its normal operation quickly. For instance, UAV-equipped cellular access points can establish a temporary network allowing radio access to users and at the same time, they can provide sensing and localization services which is of crucial importance in search and rescue scenarios. Beyond the crisis scenario, intelligent and connected networks can also boost efficiency in the ICT sector under normal circumstances. Collaborative learning and sensing are essential requirements of future smart cities which inherently come with new challenges. For instance, learning often suffers from the heterogeneity of devices in terms of computation capability and the richness of training datasets. An intelligent network can coordinate between the nodes to compensate for this heterogeneity to harmonize the 4 learning process for all nodes by optimally exchanging the data among them. Another example is self-driving cars. When it comes to autonomous vehicles, having a sufficient understanding of the environment is crucial. An intelligent network, which comprises several smart nodes including aerial access points and smart cars, can optimally exchange the data between all the cars to provide a global understanding for each of them and at the same time can improve the localization of the cars on the ground with the help of the aerial nodes. In other words, intelligent networks not only can guarantee seamless connectivity between the users but also can provide them with several other services such as accurate sensing and localization which can be a reliable alternative to the Global Positioning System (GPS).
Joint control, sensing, and communication for connected intelligence
Inspired by the examples above, this project proposes to study the design and applications of connected and intelligent networks. Given the anticipated transition to intelligent networks, the implications of this transition are expected to be in network management. Most of the management and control tasks in current networks are based on centralized decisionmaking. This makes them unsuitable to be used in future intelligent networks. In fact, what is needed is a significant redesign and reconsideration of the fundamentals and principles of distributed and collaborative network management. In particular, this project will look at the problems of cooperative sensing and control of the smart nodes in the network to provide seamless connectivity between the users as well as to offer other services such as localization when needed. We also investigate the problem of decentralized and collaborative learning between the nodes first to reduce the signaling overhead between the learners and at the same time to address the heterogeneity of the nodes. Moreover, we will lay the groundwork for developing a prototype for future collaborations. This prototype consists of several intelligent nodes/robots (e.g. UAVs, rovers) equipped with radio modules that are based on OpenAirInterface (OAI) [1] for 5G developed by EURECOM.
Overview of the state-of-the-art
Traditionally, control, sensing, and communications have been looked at separately. While the problem of controlling autonomous robots, e.g. navigation or surveillance using drones, has been studied in the robotics community [2-4], the possibilities to integrate cellularconnected robots (e.g. UAVs) attached to mobile network links or robots/UAVs providing communication services in support of terrestrial networks have been investigated separately by researchers in communications [5-7]. Moreover, in the context of 6G research, there has started a convergence between sensing and communications [7, 8], where radio nodes can also assist with sensing and localization. Contrary to static or uncontrolled mobile devices in the network, we can optimize node placement to improve the sensing and localization performance [9]. The nodes can also collaborate for further performance improvement.
A central challenge that this project will consider is to provide a collaborative and decentralized network management framework. Within this framework, each node in the network acts in a way not only to successfully perform its local task but also to contribute to the improvement of the global network performance. For instance, UAVs as flying access points in the network can optimize their locations under flying energy constraints to maintain some level of connectivity among ground users and at the same time improve the localization service provided to some of the ground nodes.
Objectives of the project
– Capitalizing on the insights and the expertise of both teams, we aim to build a new collaboration identifying the central challenges associated with future 6G connected intelligence.
– Define specific research questions that can form the basis for a larger funding proposal and seek joint funding from third-party agencies and industries.
– Develop algorithms and simulation environments for collaborative decentralized decision-making in mesh networks of autonomous agents balancing local goals and network connectivity with a focus on reducing training data demand for increased energy efficiency that are suitable for practical deployments.
– Develop joint experiments based on EURECOM’s Drone4Wireless testbed lab with a focus on disaster response scenarios, i.e. joint communications and localization of network users in peril. The prototypes developed under this lab won the “Fundamental Research Project of the Year” awarded by the French SCS (Secured Communications) Research and Industry Cluster (Pole Competitivité), 2019.
– Publish results in venues and journals of the robotics and communications research communities emphasizing the interdisciplinary character of the 6G connected intelligence challenges and fostering dialogue.
Expected impact on academia, industry and society
Societal and industrial impact
The recent public health crisis, political tensions and the European security situation, and ever-looming climate change all threaten the stability of international cooperation and trade. In the face of these events, it is imperative that Europe intensifies efforts in research relevant to critical technologies like future 6G connected intelligence. It seems likely at the same time that previous reservations about the progression of industrial automation will dissipate and industry transformation will accelerate, making the investigation of joint control, sensing, and communication problems in this context a top priority.
Research and academia
With the integration of autonomously acting agents into future communication networks, fundamentally new challenges in decision-making arise and the development of flexible MLbased algorithms becomes necessary. By highlighting the connection between the two mostly disjoint research areas of wireless communications and real-time embedded solutions for robotics and bringing them together through this project, we expect to harness the synergies that exist between these two communities of researchers, finding better overall solutions to challenges that exist in both research fields. From an academic point of view, we will publish results in venues and journals of both research communities to foster dialogue between robotic and communication research communities.
- 5G6G-TestTrack – 5G/6G Automated Driving Test Track:
Short summary and central question:
The project aims to devise and conceptually define a comprehensive extension of the above test field facilities at IABG to:
● integrate and influence the evolving 6G technologies, including connecting the testfield with Private 5G/6G testbeds currently being established by TUM (c.f. 6G-life) and EURECOM (OAI-based);
● integrate satellite and non-terrestrial connectivity for both communication and navigation capabilities into the test field and establish means countering attacks on either digital infrastructure;
● research on secure and safe AI to ensure trustworthy operation by extending the capabilities of sensors and their sensed data (e.g., watermarking or fingerprinting), ensuring trustworthy computation (e.g., by means of trusted execution environments), and robust AI models for data processing; and
● research on the digital representation of vulnerable road users, especially the interaction of automated vehicles with cyclists and pedestrians.
With this, the 5G/6G-TestTrack project will develop an unprecedented test facility for true co-evolution of both communication & sensing infrastructure and traffic control / automated driving.
The overarching research question connecting all the above aspects will be: how to achieve resilience within and across digital infrastructures from sensor to network to AI to actuator for future fully digital mobility and traffic control systems.
Overview of the state-of-the-art
Quite a few 5G testbeds were set up over time (Oulu Univ, Aalto Univ, EURECOM, among others) and open source platforms were developed for building such (most notably the OpenAirInterface (OAI) by EURECOM https://openairinterface.org/). Numerous efforts for 5G network performance measurements were carried out (e.g., https://dl.acm.org/doi/10.1145/3366423.3380169 published at The WebConference 2020 or https://dl.acm.org/doi/10.1145/3452296.3472923 at SIGCOMM 2021). At the same time, many commercial and academic efforts on aspects of autonomous driving (from sensing technologies to AI for object detection to inter-vehicular communication to trajectory planning to safely functions to teleoperated driving) have been underway. Similar efforts exist on traffic control, V2I interaction, and support for vulnerable road users, quite often focusing on simulations, however.
The driving testfield and networking testbed being established offer the unprecedented opportunity to carry out joint research and productization in an interdisciplinary setup.
Objectives of the project
The main objectives of the 5G6G-TestTrack project are:
- defining a comprehensive and detailed concept for an extension of the present driving testfield at IABG and its integration with Private 56/6G testbeds to create a testfield version 2;
- identifying (possibly pretesting) the necessary target equipment to evolve the present version 1 of the testfield into a version 2 with enhanced sensing and networking capabilities, integrated with a private 5G/6G testbed.
- Defining a comprehensive research agenda that covers for the testfield version 2:
○ Sensing, actuation, and continuous measurements 3
○ Networking, satellite integration, and testbed integration ○ Resilient GNSS for assisted and automated driving and traffic control
○ Safe and secure AI for the complete processing pipeline from the sensor to the actuation
○ Traffic control and support for vulnerable road users
- Devise a concept for the design and implementation of a validation and certification service for assisted and automated driving The ultimate goal is creating a project proposal
application to fund the research areas defined above including the equipment necessary to evolve the testfield to version 2.
Expected impact on academia, industry and society
The testfield to be developed will support furthering the development of assisted and automated driving by co-evolving vehicular, sensing, networking, and traffic control technologies. Digitization of traffic and mobility (of people and goods) will be a key contributor to pressing societal demands:
● Assisted and automated driving including traffic control will reduce carbon emissions, reduce traffic and traffic jams, reduce accidents, and contribute insights to extending the reach (not just) of electric vehicles through improved traffic flow.
● The inclusion of vulnerable road users will help protecting those weakest traffic participants and reduce injuries and casualties.
● Extending connected mobility to embrace satellite technologies will finally be able to provide the necessary complete coverage of even remote regions and thus enable service beyond the otherwise well connected metropolitan areas, supporting digitally enabled equality of all citizens.
● Ensuring security and resilience to attacks of the involved technologies, especially concerning GNSS-based operation.
● Fostering safe and secure – trustworthy – AI operation, which is key to the acceptance of these technologies as AI has a critical role in automated driving.
● The testing and certification facilities will support know-how build-up in for the involved parties and thus the region and accelerate technologies assessment and trials for France and Germany.
- DARWEEN Data Acquisition and Processing Framework for the Efficient Ecological Assessment of Manufacturing Systems
Short summary and central question:
Recently, the European Commission presented a proposal to amend the CSR Directive, which will put sustainability reporting on an equal footing with financial reporting. In addition, as part of the regulation for an eco-design for sustainable products, a framework to set ecodesign requirements for sustainable products has been initiated (EUROPEAN COMMISSION 2022) to provide consumers with better transparency in their purchasing decisions. In this context, several environmental parameters, such as the amount of used recyclable materials and substances, the consumed energy, generated waste and emissions as well as the environmental and carbon footprint, were determined as a basis for the evaluation of products. In contrast to indicators of production performance, the necessary data for an environmental assessment are as of now not recorded continuously but only for specific cases, such as the need for certification (FERRARI ET AL. 2021). This is especially due to the time-consuming data collection during the Life Cycle Assessment and the required expertise to identify the needed parameters (MEINRENKEN ET AL. 2012). With a variety of possible metrics and scoring systems, companies are unsure how and which parameters must be gathered to meet regulatory data needs in the production environment. The aim of the project is therefore to develop a framework that supports companies in deciding which data should be recorded at which location in a production company in order to collect all the necessary ecological information of a product as efficiently as possible. The central question that is to be answered in the context of this project is therefore: How must a framework be designed to support manufacturing companies in deciding which data for the ecological evaluation of products and its manufacturing processes should be gathered at which location in the company and how can the data gathering be designed as efficiently as possible?
Overview of the state-of-the-art
While several research projects address environmental factors and KPIs, such as energy minimization (LAMY ET AL. 2020, GIANESSI ET AL. 2021), the used parameters mostly rely on built data, as collecting and identifying relevant ones is challenging. For the evaluation of the ecological impact of a product, the procedure of a life cycle assessment study is used, which is regulated, among others, in ISO 14040, which is one of the most well-known standards. In four phases, the scope of the LCA study is defined, the necessary data are recorded in the life cycle inventory (LCI) phase, an impact assessment (LCIA) is carried out, before an evaluation is performed finally (ISO 14040 2021). While the methodological procedure is already regulated by ISO 14040, there is a lack of standards for the necessary accuracy in data recording, which is why current LCA studies in research have different levels of detail and companies are unsure about what data to include in their assessments. 4 Moreover, small companies in particular lack the necessary expertise and investment to carry out data collection in a targeted manner, which is why LCA is often only carried out in larger companies (SUREEYATANAPAS ET AL. 2021). Due to the complexity of performing an LCA, simplification has become a widely used approach. Particularly relevant simplification logics are exclusion, inventory data substitution, qualitative expert judgements, standardization and automation (BEEMSTERBOER ET AL. 2020). Simplification makes it easier for companies to perform LCAs, but it leads to a loss of information, is dependent on trained experts, and still requires the recording and assessment of basic data. Therefore, in conjunction with stricter regulations and the goal of increasing LCA automation, data collection during LCI remains one of the biggest barriers in ecological assessments.
Objectives of the project
The overall objective of the project is the development of a framework that supports manufacturing companies in the data gathering of ecological parameters by identifying and classifying the appropriate data needs. In addition, the required data gathering should be optimized by identifying suitable methods, that could be applied in an increasingly networked production environment. These goals are intended to further integrate the issue of sustainability into the digital transformation of manufacturing companies. The project objective comprises the following sub-goals:
- Development of a parameter catalogue containing the relevant data needs for determining the ecological impacts of manufacturing processes in mechanical and plant engineering
- Description of a classification system for the identified parameters in terms of the need for data recording in the manufacturing process or at plant level in relation to the maturity phase of manufacturing processes
- Proposition of a framework that companies can use to structure the identification of necessary ecological parameters and their collection
- Identification of technological possibilities for data acquisition of individual parameters
- Design of a roadmap to enable companies to apply the framework in their business context
Expected impact on academia, industry and society
As the society influences consumer-driven sustainability measures throughout the supply chain (MORAN ET AL. 2020), producing products with a reduced ecological impacts can become a key competitive advantage for manufacturing companies. The reduction of a product’s ecological impact enables companies to promote product differentiation and to enhance a company’s brand image (SUREEYATANAPAS ET AL. 2021). At the same time, the pressure on companies to determine their ecological impact continues to increase due to stricter regulations. For this reason, the assessment of the ecological impact is not only a potential competitive advantage but also a risk at the same time.
To support companies in the challenge of ecological assessments and thus the transformation to a more sustainable society, a targeted data collection in production processes is required, for which the basis is to be laid within the framework of this research 5 project. Optimizing data analysis makes life cycle assessment easier for companies and leads to increased transparency in products.
The identification of the necessary parameters supports science in the development of a standardized procedure in the ecological assessment of products and thus leads to a better comparability of further studies. Furthermore, the procedure developed on the basis of the identified ecological parameters in manufacturing processes can be transferred to other relevant areas like the assembly or transportation processes and thus leads to more transparency in the different assessment methods.
- Data-Driven Dynamic Resource Management for Random Time-Varying Demands in the Context of Covid-19 and future crises
Short summary and central question
Capacity planning and resource management is a fundamental global challenge in the face of global crises such as the Covid-19 pandemic. One needs to understand the variabilities in time-varying demand for resources to hedge against the random demand fluctuations and take advantage of the risk-pooling effect. Covid-19 predictive and machine learning models used in capacity planning typically predict the mean values of the demands in both the temporal and spatial dimensions. However, they seldom provide reliable predictions or estimations of demand variabilities and, therefore, are insufficient for proper capacity planning. Some works focus on forecasting the evolution of the epidemic based on limited historical data and the resource needs over the days and weeks to come. However, the diversity of decisions, system dynamics over time, and demand uncertainties make decisions challenging even with good forecasts.
During the crisis, regional or national resource pooling and allocation must be planned. Healthcare providers should identify their maximal capacity and decide how much capacity to share with others. National strategic reserve resources should also be planned, and emergency resources should be obtained from every organization. Once the capacity of the healthcare providers has been identified, and the sharing resources are allocated, the dynamic management of the resources must be put in place according to the epidemiological development.
It is a question of managing the allocated capacity as well as possible according to the progression of the crisis and the limited available data and information. The number of confirmed cases, the number of hospitalizations (e.g., intensive care patients), the number of deaths, etc., are the primary demand data, which in fact may be underestimated during the crisis. There is also a time lag between the data collection and the actual crisis development, making demand prediction even more difficult. On the other hand, the number of places open for infected patients in intensive care and hospitalizations is the major supply data. These resources are counted with material, equipment, consumables, and personal support. The organization of staff working time must therefore be integrated into these decisions. Resources such as mobile resuscitation units from the military would be managed at this level.
Using data analytics, machine learning predictive models, and data-driven optimization methods, it appears that playing on the management of resources according to the evolution of needs linked to the epidemic would make it possible to utilize better the limited resource and hedge against the temporal and spatial prediction uncertainties.
Overview of the state-of-the-art
Resource management in crises is an emerging topic and has become even more critical given the recent Covid-19 pandemic and wars, as well as other ongoing and future crisis: climatic change, social inequality, and other planet-wide crises (Marco et al. 2020; Zhu and Xie, 2020; Azizi et al. 2021; Chumachenko and Chumachenko, 2022). Efficient and timely hospital resource management can mitigate the negative impact of a pandemic on public health (El-Rifai et al., 2016; Khichar et al. 2020). However, it is very challenging because of the dynamic random epidemic evolution, unpredictable and time-varying demands and limited resources. Researchers have developed advanced methods to deal with these difficulties. For instance, Pehlivan et al. (2014) study hierarchical service networks’ dynamic 4 capacity planning and location. With time-varying uncertain demand, Liu and Xie (2018) study human resource planning for emergency care. The recent Covid-19 pandemic has boosted this stream of research based on data analytics. Among those, Hong et al. (2022) study the demand variability scaling and capacity planning in the Covid-19 pandemic. Xie et al. (2022) use advanced analytics to manage hospital resources when facing bed shortages. However, most existing literature applies the traditional “data prediction and estimation, then optimization” framework. This project aims to develop a novel data-driven decision-making tool that directly transfers the data into optimal, robust and easy-to-implement decisions in future unknown crises.
Reference
Pehlivan, C., Augusto, V., & Xie, X. (2014). Dynamic capacity planning and location of hierarchical service networks under service level constraints. IEEE Transactions on Automation Science and Engineering, 11(3), 863-880. El-Rifai, O., Garaix, T., & Xie, X. (2016). Proactive on-call scheduling during a seasonal epidemic. Operations Research for Health Care, 8, 53-61. Liu, R., & Xie, X. (2018). Physician staffing for emergency departments with time-varying demand. INFORMS Journal on Computing, 30(3), 588-607. Di Marco, M., Baker, M. L., Daszak, P., De Barro, P., Eskew, E. A., Godde, C. M., … & Ferrier, S. (2020). Opinion: Sustainable development must account for pandemic risk. Proceedings of the National Academy of Sciences, 117(8), 3888-3892. Zhu, Y., Xie, J., Huang, F., & Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the total environment, 727, 138704. Khichar, S., Midha, N., Bohra, G. K., Kumar, D., Gopalakrishanan, M., Kumar, B., … & Garg, M. K. (2020). Healthcare resource management and pandemic preparedness for COVID-19: a single centre experience from Jodhpur, India. International Journal of Health Policy and Management, 9(11), 493. Azizi, M. R., Atlasi, R., Ziapour, A., Abbas, J., & Naemi, R. (2021). Innovative human resource management strategies during the COVID-19 pandemic: A systematic narrative review approach. Heliyon, 7(6), e07233. Chumachenko, D., & Chumachenko, T. (2022). Ukraine war: The humanitarian crisis in Kharkiv. BMJ, 376. Hong, L. J., Liu, G., Luo, J., & Xie, J. (2022). Variability Scaling and Capacity Planning in Covid-19 Pandemic. Fundamental Research. Xie, J., Loke, G. G., Sim, M., & Lam, S. W. (2022). The Analytics of Bed Shortages: Coherent Metric, Prediction, and Optimization. Operations Research.
Objectives of the project
This project aims to develop data-driven decision-making tools for dynamic hospital resource management under a significant, long-lasting, widespread crisis such as a pandemic.
Expected impact on academia, industry and society
The project will impact academia by developing data-driven decision-making tools to support dynamic resource management under uncertainties and time varying demand, which has proved to be very challenging. This project aims to develop a novel data-driven decisionmaking tool that directly converts the data into optimal and easy-to-implement decisions in future unknown crises, while most existing literature applies the traditional two-step “data prediction or estimation, then optimization” framework. Timely and effective resource planning and management can help society combat the crisis and minimize the negative impact on public health and the economy.
- ENABLEI – Identify and overcome barriers to enable the use of Human-Robot Collaboration in Industry
Short summary and central question
Human-Robot collaboration aims at combining the strengths of robots, like high speed and good repeatability, with the flexibility and adaptability of human workers. These systems can support humans when performing physically challenging tasks and at the same time allow automation in scenarios where this was previously considered unfeasible without the capabilities of collaborative robots (short: cobots). As a result, complex manufacturing processes can be carried out in high-wage countries such as France and Germany, even as the shortage of skilled labor continues to rise. The use of collaborative robots could consequently contribute to social and economic sustainability within these European countries.
However, despite these benefits and despite being discussed in the scientific literature for many years, the systems are hardly used in industry today, especially when the robot is mobile in the industrial environment. Therefore, we propose to investigate the reasons for this in order to identify future research and development needs. The approach of this French-German project can be summarized as follows: After elaborating the state-of-theart, an extensive industry survey will be conducted to determine obstacles for and during the implementation of human-robot collaboration applications in high- and low-volume 4 production and service environments. Based on the generated insights, a future research agenda for funding opportunities such as Horizon Europe can be defined.
Overview of the state-of-the-art
Introduction
Recently, the European Commission started a complementary approach to Industry 4.0, called Industry 5.0. It is a transformative vision of the European industry, moving toward more sustainable, human-centric, and resilient systems [1].
In this new paradigm, human-robot collaboration refers to environments, where humans and robots work in close proximity, sharing their workspaces, resources, or even their tasks [2]. For the scope of this project we defined the term in a rather broad sense, including different systems that involve direct interaction between humans and robots. In the following, we will briefly address the state of the art regarding robot applications, the use of industrial robots for robot applications, mobile collaborative robotics, mobile robot manipulators as well as the role of machine vision in this context.
Human-robot collaboration with robots
Early works on collaborative robot applications date back to the year 1996 and 2001 [3, 4] and aimed at increasing ergonomics for human workers. Today, the term robot is mostly associated with lightweight robots, specifically designed for collaborative applications, like the UR5 or KUKA iiwa [5]. Their low weight in combination with limited speed results in lower forces during motion and, therefore, decreases the risk of injuries for workers, in case of contact. Furthermore, they are equipped with various sensors, such as gear torque sensors in the robots’ joint angles. Robot applications can be distinguished according to their level of collaboration. The Fraunhofer IAO defines with increasing levels of collaboration: Cells, Coexistence, Synchronized, Cooperation, and Collaboration [6]. Safety aspects are of utmost importance for the acceptance of this technology [7] and for the commissioning of such systems, various normative requirements must be met [8, 9]. In practice, the level of actual interaction between robots and humans is usually still quite limited [7].
Collaborative applications with industrial robots
One main drawback of robots, compared with industrial robots, is their limited payload capacity, execution speed, and precision [7]. Therefore, using conventional industrial robots in collaborative applications yields further potential [10]. In this case, safety aspects can be met using external sensors. For example, vision systems can be used to monitor the workspace for human workers [11] or additional robot “skins” (sensitive covers) can detect collisions before critical forces occur [12].
Mobile Collaborative Robotics
Mobile robot systems must continuously plan their path to the tasks to be performed. An emblematic scenario is order preparation in a warehouse environment, like the Ocado or Exotec solutions [13, 14], which reach a high level of efficiency. Here, restricted areas are defined for robots to operate without humans in the loop. Operators interact with the autonomous system only within specific workstations. But whatever the industrial scenario looks like, introducing mobile robots in a shared human-robot environment requires the 5 ability to integrate humans (co-workers, visitors, drivers of non-autonomous systems), anticipate their behaviors, and to adapt the robot motions consequently. However, Multipath Planning is already a challenging problem to solve [15]. In order to exploit the full potential of autonomous mobile robots in industrial environments, the uncertainty of human actions must be modeled and the robot paths have to be dynamically adapted accordingly.
Mobile Robot Manipulators
The integration of the human behavior in the robot decision process is of prior importance in collaborative task scenarios where the mobile robot and the human work with a close and continuous interaction [17]. Mobile robots and Mobile Robot Manipulators could replace the traditional conveyer to transport parts from one workstation to another and could assist operators in their workload. The use of such mobile solutions prevents the blocking and starving of workstations and leads to more flexible manufacturing systems. When designing and implementing Mobile Robot Manipulator applications, it is recommended to also consider the ergonomics of the workers to prevent musculoskeletal disorders (MSDs) and to maximize the performance of such a collaborative work environment [18, 19].
Machine vision in human-robot collaboration
Machine learning and artificial intelligence have allowed considerable advances in computer science. However, its industrial adoption in the domain of robotics is still in its early stages. A review of recent achievements is available in [20]. A number of elementary tasks still remain open: distinguishing backgrounds and objects, identifying moving objects, identifying partially covered objects, recognizing changing shapes or articulation or understanding the position and orientation of objects. All these tasks are required to enable the following paradigms for human-robot collaboration:
• safety ̶collision avoidance (with humans and obstacles)
• coexistence ̶the robot capability of sharing the workspace with other workers
• collaboration ̶capability of performing robot tasks with direct human interaction and coordination
The presence of humans in the workspace or the random presence of objects to be manipulated introduces uncertainty that requires sensor inputs for robot control. Four sensory modalities have become dominant: vision, touch, audition, and distance. The project partner CMM (MINES Paris) brings expertise within the embedded vision and distance sensing modality [23] into the consortium. Both can be mounted on moving parts of the robot or fixed in the workspace (referred to as “eye-in-hand” and “eye-to-hand” visual servoing, respectively). The sensing can be used for actuation, servoing, trajectory planning, and security [21]. The sensor inputs are translated to servoing commands via the classic inverse kinematic problem [22] or recently, using reinforcement learning.
Lidar scanners provide 3D perception to the robot. Their active components make them more robust than image-based 3D reconstruction counterparts. Recent solid-state lidar technology has been used to produce low cost scanners with appropriate specifications for mobile robotics applications.
Objectives of the project
This project has the following three main objectives:
- Elaboration of the state-of-the-art regarding human-robot collaboration:
• Similarities and common design patterns in approaches presented in scientific literature should be identified.
• Interesting and competitive best practice applications from academia and industry will be defined.
- Industry study and identification of obstacles:
• Barriers for human-robot collaboration in industry should be identified from an industry perspective.
• The results should be aggregated and compared, e. g. regarding companies from different industries, of different sizes, and from different countries (e. g. France, Germany, further European countries).
• Gained insights should be shared with the scientific community and industry by publication of a joint research report and conference papers.
• If necessary, proposals for the adjustment of regulations that unnecessarily restrict the use of human-robot collaboration will be developed. Furthermore, the differences in regulations between France and Germany will be identified in order to support the path toward a unified European economy.
- Definition of future research agenda:
• Opportunities for further developments of human-robot collaborative systems, both in terms of the technical capabilities of the systems as well as their embedding in surrounding manufacturing systems, should be identified.
• Shared and complementary know-how of project partners will be identified, and their research activities synchronized.
• Based on the above, a future research agenda will be developed, and further funding opportunities will be investigated.
Expected impact on academia, industry and society
Industrial Application and Society
We expect to give new impulses for academic research on systems involving human-robot collaboration and to identify research questions with high relevance for industry. Therefore, industrial companies might benefit from solutions tailored toward their specific needs. This contributes to the economic and social sustainability of the manufacturing industry. Furthermore, entry barriers into human-robot collaboration can be lowered for companies from different countries by pushing for uniform regulations.
Academia
Identifying potential industrial application scenarios for collaborative human-robot systems can open up new research questions regarding human perception, integration of human partners in the robot’s decision-making process, safety, etc. The proposed project will help in the definition of specific scenarios and challenges to tackle. This helps researchers to adapt their research activities according to industry requirements in order to improve the state-of-the-art for autonomous robotic systems.
- Q-IMTUM: Quantum IMT/TUM Network design
Short summary and central question
The foreseeable breakthrough of quantum computers represents a risk for all communication systems. In order to ensure long-term communication security, quantum secure communication must be realized, where security is guaranteed by physical principles. Quantum cryptography plays a critical role in the protection on the quantum era, promising to ensure ultra-secure communication while supporting crisis resolution, disaster recovery and relief and prevention. Clearly, the use of quantum technologies to provide protection against attacks and cybercrime is essential. However, the end-to-end quantum network design able to support quantum key distribution service and management is yet to be realized. The quantum network is designed as a layered approach, where the physics, defined in the quantum layer, are used to provide secure keys (key management layer) for different cryptographic functionalities, monitored by the manager layer and where the communication routes are establish by the control layer so as to ensure the security and performance for the different users. Moreover, quantum networks need be designed scalable and it is foreseen to be able to operate as a commodity in traditional networks (as an additional layer able to provide differentiated security services).
The goal of the project is to explore the design space of the quantum, control and management planes of the quantum network so as to ensure a high security and high performance. The quantum layer (devices and protocols) should be efficient and secure, while the integration with current networks should be smooth.
Overview of the state-of-the-art
Different demonstrators have proven the security characteristics of the quantum networks. Europe has committed to the development of such networks to ensure the sovereignty and security of Europe. Airbus Defence and Space is a major player of such an efforts, currently being responsible of the first design of the European quantum network (EuroQCI). Nevertheless, there is still a huge design space to explore. It is expected that European academic/industry efforts are join to pursue this goal.
Quantum Key Distribution (QKD) exploits quantum physics to securely transport secret keys. Such keys are later on used to preform different cryptographic operations (encrypt, decrypt, sign, verify). The implementation of such networks include the definition of a quantum, key management, management and control layers. The state of the art focused mainly on point-to-point link for the exclusive transport of quantum signals. The integration of the quantum plane in classical networks in a real-world shared network has not received sufficient attention so far. The goal of our project is to pursue current work and further develop the quantum layer (so as to improve the performance and security), while integrating it into classical networks (including a complete management framework). The latter has to include the four planes needed for smooth integration, that is the quantum management plane, the control plane, the key management plane, and the underlying quantum plane. Quantum layer is constantly evolving. It includes different technologies and protocols (prepare and measure, Measurement device independent, entanglement) that achieve different rates of performance and security characteristics. Novel materials and devices are now developed so as to enhance these properties.
There has already been research and design initiatives to develop and produce an SDN based control plane for quantum communications configuration and control and for integration in existing traditional networks [1-4]. Despite these efforts, the production of the four planes and the focus on their coordination and joint management is barely addressed thoroughly. This project, building on previous experience, from partners, on developing a full blow management system for optical switches and disaggregated switches [5-9], aims at reproducing the accomplishment for quantum communications and for control and management of the quantum plane. In [8], a Micro-services Optical Network Controller Platform (μONCP) that implements the deployment, configuration and control workflows (thus includes a workflow engine and an orchestrator) of optical switches is reported. The goal in this new project, here, is to design not only the SDN based control plane but also a management plane that is capable of realizing zero-touch quantum communications workflows and automatize their execution in real and operational networks. The issue consequently for this project is to produce the 4 complete framework and the northbound applications that come along with it to optimize jointly (in possibly one shot/step) the key distribution, generation, allocation and routing. I addition, we will address the specification of all interfaces between the four planes and expect to rely on well-established and partially standardized interfaces: RestFul, OpenConfig, NetConf, Yang and TAPI as well as rely on those that are quantum plane specific such as QuAI [10]. This interface, embedded in SDN agents, enables vendor agnostic configuration of QKD devices and provides a flexible procedure to integrate QKD devices in production networks.
[1] A. Aguado et al., “The Engineering of Software-Defined Quantum Key Distribution Networks,” in IEEE Communications Magazine, vol. 57, no. 7, pp. 20-26, July 2019.
[2] D. Lopez et al., “Madrid Quantum Communication Infrastructure: a testbed for assessing QKD technologies into real production networks,” 2021 Optical Fiber Communications Conference and Exhibition (OFC), 2021, pp. 1-4.
[3] A. Aguado et al, “Enabling Quantum Key Distribution Networks via Software-Defined Networking,” 2020 International Conference on Optical Network Design and Modeling (ONDM), 2020, pp. 1-5
[4] Y. Cao et al, “The Evolution of Quantum Key Distribution Networks: On the Road to the Qinternet,” in IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 839-894, Secondquarter 2022.
[5] Q. P. Van, D. Verchere, et al, “Container-Based Microservices SDN Control Plane for Open Disaggregated Optical Networks,” 2019 21st International Conference on Transparent Optical Networks (ICTON), 2019, pp. 1-4.
[6] Q. Pham Van et al, “Demonstration of Container-Based Microservices SDN Control platform for Open Optical Networks,” Optical Fiber Communications Conference and Exhibition (OFC), 2019, pp. 1-3.
[7] Q. Pham Van et al., “Monitoring Intent for Optical Channel Defragmentation in Software-Defined Elastic Optical Networks,” International Conference on Transparent Optical Networks (ICTON), 2018, pp. 1-4.
[8] Van-Quan Pham, Cloud-Native Optical Network Automation Platforms, Thesis manuscript, 2022
[9] H. T. Quang, Q. Pham-Van, D. Verchere, H. -T. Thieu and D. Zeghlache,”Demonstration of ML-aided Impairment-aware L0 Path Computation in Fully Disaggregated Multi-vendor Optical Transport Networks,” 2021 Optical Fiber Communications Conference and Exhibition (OFC), 2021, pp. 1-3.
[10] R. B. Mendez et al., “Quantum Abstraction Interface: Facilitating Integration of QKD Devices in SDN Networks,” International Conference on Transparent Optical Networks (ICTON), 2020, pp. 1-4.
Objectives of the project
The objective of the project is to design and integrate quantum communication technologies in current computer and telecommunications networks using SDN-based control planes augmented with the appropriate northbound applications. It includes the exploration and design of an efficient and secure quantum layer together with the design of the orchestrators and quantum management planes necessary to enable integration of quantum systems in current networks. All of which are required to foster the integration of quantum networks and systems in current infrastructures. The project will contribute to the longer term that consists of enabling smooth transition to a technology mix.
More specifically, the project will focus on Airbus use cases and scenarios exploring the use of ultrasecure end-to-end communication based on cryptographic key transport through Quantum Networks. The project will consequently pay attention and work on quantum keys generation, placement, scheduling and in summary manage their entire life cycle since the quantum keys are principal resources in quantum networks.
Beyond typical and current research and investigations that focus on the quantum building blocks, this project includes all communication and management layers. The project takes into consideration the following planes: the Quantum Plane itself, the Key Manager (KM) Plane, the Control Plane and the Management Plane.
The project was structured around this broaden scope that leads to the following tasks, objectives and research activities:
- Define a methodology and method for fast exploration of quantum networks to meet performance, security and cost requirements;
- Design an efficient control plane that considers and embeds operational needs;
- Propose a first concept for a network manager to monitor the health of the quantum system;
- Propose a first concept of a secure and efficient quantum key management system
Expected impact on academia, industry and society
From the academic standpoint, addressing the design and integration of quantum technologies in current networks and designing a control plane that enables an optimal integration will reinforce and foster the development of skills and known how on quantum networks. Especially increases knowledge in controlling, configuring and managing these quantum networks in an integration and transition context from non-quantum networks to a mix of quantum networks with traditional networks (including optical). From the industrial standpoint quantum networks are simply strategic, critical for Europe (in our case at least Germany and France) to remain competitive and aim at leading the emergence of such technologies. From a societal standpoint, quantum networks have the potential to mitigate and reduce considerably the impact of cybercrime and attacks on citizens and persons, provide stronger security and protect privacy and assets. Harnessing quantum networks and their integration into our infrastructure and life is strategic and fundamental for the decades to come. In addition, European commission already establish critical to develop this technology on Europe so as to keep competitive and ensure the sovereignty. This project is aligned with this claims.
- RAMP-UP II ± Supporting agile ramp-up and ramp-down in service and manufacturing industries
Short summary and central question
RAMP-UP II project contributes to addressing the limitations of state-of-the-art approaches for agile, resilient and sustainable manufacturing and service industries, in particular in uncertain situations such as crisis context. The focus of the maturation phase is on manufacturing and healthcare services. The main objective of RAMP-UP II is to develop a generic methodology supported by decision-support tools to plan and manage ramp-up and ramp-down projects considering resilience, agility, and sustainability criteria.
Overview of the state-of-the-art
Ramp-up management refers to the value creation phase starting with the completion of product and process design and ending with the achievement of the full production capacity (Schuh et al., 2015; Wochner et al., 2016). This phase plays a crucial role in the successful introduction of products or services into markets. While ramp-up is expected to support flexibility in volume and assortment, it is hindered by several challenges such as complexity of products, services, and production systems. Moreover, this phase is characterized by uncertainty as well as a dynamic environment with mostly untested processes, thus, (positive and negative) risks are high (Ball et al., 2011; Schuh et al., 2015). Recent advances in ramp-up management in product domain involves capacity investment/expansion (Hansen and Grunow 2015; Medini et al. 2020; Pierné et al. 2020; Riffi-Maher and Medini, 2021), product design and functional requirements fulfilment (Kukulies and Schmitt 2018), inventory management and supplier selection (Glock and Jaber 2013), workforce and qualifications (Glock et al. 2012), quality management (Colledani et al. 2018), and information and collaboration (Fjällström et al. 2009).
The COVID-19 sanitary crisis uncovered the difficulty in timely responding to high-volume demands of specific products such as Personal Protective Equipment (EPI) and resuscitative equipment, as well as hospitals capacity. Outsourcing has shown its limitations since the delocalized industry is not performing as expected within the current global crisis (e.g., changing priorities, very tough competition, supply chain disruption). These problems involve not only products but also a variety of services in second and tertiary sectores alike. In the manufacturing domain, spare parts’ shortages affected equipment maintenance services and led to further shortages of products in other markets. Similarly, during the pandemic, healthcare facilities and their related (value) networks undergone critical situations where the demand (patients and equipment) outreaches service capacity and difficult decisions had to be taken. However, looking into the literature, it can be reasonably concluded that service ramp-up and ramp-down and service agility at large have been overlooked compared to product domain (Lenfle and Midler 2009; Christensen 2018; Akkermans et al. 2019). Further on, in both product and service contexts, ramp-up projects often target increasing volumes, speeding up processes, and in some cases improving quality. These targets could only partly support resilience, agility and sustainability and could even be conflicting with these criteria (e.g., useless volume increase, misalignment with market/customer requirements, etc.).
Existing and innovative approaches need to be combined in order to ensure resilient, agile and sustainable value networks at given territorial levels in uncertain environments such as time of crises. For instance, Vogel-Heuser et al. (2020b) proposed model-based engineering for successful management of requirement changes. The proposed method can be extended and combined with other aspects such as resource management to deal with risk management in crises. Furthermore, the proposed BPMN model introduced in (Vogel-Heuser et al., 2020a) for cooperation in working groups and between companies, can be enriched in this project to model and support decision-making processes on a management level in crises. Additionally, existing technologies in Industry 4.0 can be utilized for decision-making and optimization in production. For instance, multi-agent systems (MAS) can increase the decentralization, robustness, 5 flexibility, and autonomy of the production system (Leitão and Karnouskos, 2015; Medini et al., 2021). Therefore, MAS architecture can increase the flexibility during the crisis as well and improve the decisionmaking process. For production optimization, the MAS suggested by Rehberger et al. (2017), Kovalenko et al. (2019) and Medini et al., (2021) can be further extended for production ramp up and ramp down during the crisis. In order to utilize the MAS, Ocker et al. (2019b) introduced a semantic web ontology to facilitate the communication between agents. Ontology as a method for representing and sharing the knowledge of the MAS can be used to model the capabilities of a production system and support the ramp up of new products and services during crises. The current state-of-the-art does not bring sufficient answers to the following general problems:
- How to address service and production ramp-up and ramp-down management?
- How to cover resilience, agility and sustainability criteria when addressing ramp-up?
- How to support ramp-up/ramp-down management with practical decision-support tools?
The teams involved in the project have deep expertise in the relevant topics supporting the successful achievement of the project objectives, which will be introduced in the following.
Objectives of the project
RAMP-UP II will contribute to the following objectives (O):
O1. Development of a ramp-up/ramp-down management approach allowing for resilient and agile value networks, particularly, in uncertain and dynamic environments.
O2. Integrate resilience, agility and sustainability related indicators for planning and managing rampup/ramp-down projects.
O3. Develop decision-support tools allowing to implement and assess alternative collaboration strategies and decentralized decision making in dynamic environments.
O4. Modelling and representation of knowledge about stakeholders involved in value network.
To address these objectives, a multi-disciplinary approach is necessary, combining expertise in industrial management (e.g., O1, O2), multi-agent systems and artificial intelligence (e.g., O3), enterprise modelling, knowledge representation, automation and data management (e.g., O4).
Expected impact on academia, industry and society
Scientific impact :
- Conceptual and methodological contributions in relation to the practices and general guidelines for managing ramp-up/down project in manufacturing and service industries, in particular in uncertain environments, such as crisis context.
- Providing decision-support tools relying on simulation, multi-agent systems and artificial intelligence to assess alternative resilient, agile, and sustainable ramp-up/down strategies.
- Gaining insights into the challenges for building a digital-twin of the value network for real time management of service ramp-up and ramp-down.
Society and industry impacts:
- Improve resilience, agility and sustainability of manufacturing and service industries.
- Reinforce vertical integration of value networks through close collaboration and resource sharing.
Promote the adoption of decision-support tools relying on simulation, multi-agent systems and artificial intelligence among SMEs and large compa
