Welcome to Chair of Cyber-Physical Systems in Production Engineering!

    Chair of Cyber-physical Systems in Production Engineering, led by Prof. Dr. Caccamo (Holder of Alexander von Humboldt Professorship in 2018), was founded in September 2018. We focus on designing safe, predictable, and high performance embedded platforms for next generation Cyber-Physical Systems, in which computing, communication, and control technologies are tightly integrated. Applications include system automation, Internet of Things (IoT), smart buildings, smart manufacturing, smart cities, digital agriculture, robotics, and autonomous vehicles.

Research Thrusts


Predictable Software Integration on COTS Multi-core Architectures

    Multi-core architectures are shaking the very foundation of modern real-time computing theory. Much of the real-time scheduling theory in the past two decades was based on the assumption that we can compute the Worst Case Execution Time (WCET) of each task when it is executing in isolation. When tasks are executing together, scheduling theory would compute the worst case response time as a function of the run-alone WCETs. Unfortunately, this fundamental assumption is not even true in an approximate sense on a modern multicore chip, and often this leads to a costly integration phase for complex real-time embedded software.
    State-of-the-art practice in safety critical embedded industry is to use only one-core of a multi-core chip. While some ad-hoc isolation solutions have been proposed to use more cores of a chip when running safety critical software, there is no publically available and validated procedure to assess the quality of proposed isolation methods or evaluate the safety and real-time performance of the integrated hardware-software system. Transformational research by the academic community in collaboration with certification authorities and industry is urgently needed to overcome present challenges faced by safety critical embedded industry. As an example, certifiable resource isolation technology for multi-core will be imperative for the automotive industry to guarantee the successful integration, verification, and testing of fully autonomous driving systems. Smart manufacturing is facing similar challanges. In fact, Artificial Intelligence (AI) algorithms will be soon integrated with the embedded software that currently controls manufacturing robots. These augmented CPS systems will generate increasing volumes of real-time (imaging) data flows causing the hardware memory hierarchy (the DRAM and the cache hierarchy, especially the last level cache shared among multiple cores) to become a bottleneck resource and a source of temporal unpredictability. Current research focuses on novel task execution models and co-scheduling policies to restore the temporal predictability of safety critical software running on last generation heterogeneous multi-core platforms (e.g., Xilinx Ultrascale+ includes different types of cores, hardware accelerators, and programmable logic).
    We aim to work closely with leaders in manufacturing, automotive, and avionics industries, organizing tutorials and workshops on the topic of “Real‐Time and Parallel Computing on Multi-Core Processors”, bringing together the real-time academic community, the embedded industry, and the certification authorities for electronic hardware and software systems to tackle an industry-wide challenge: how to design, integrate, analyze, and certify safety-critical real-time software that runs on multi-core processors.


Sandboxing Software Controllers to Design Safe and Secure Cyber-Physical Systems

    Modern Cyber-Physical Systems (CPS) are large complex systems of systems, where arguments about the behavior of the whole system rely on guarantees about the individual components. Individual components, however, may be designed using machine learning methods such as neural networks that are currently not amenable to formal analysis, or the components may simply be too large and complex for complete verification. Hence, it is increasingly difficult to ensure the safety and security of CPS.
    To cope with this challenges, we explore new sandboxing mechanisms to design novel software architectures such that a cyber-physical system can use unverified controllers, but its safety does not depend on it. At the core of this technology, there are a Safety Advisor and a Supervisor (Safe-visor). On one hand, Safety Advisor only focuses on the safety of the system, which should be treated as a fallback in case the unverified controller is trying to perform some harmful actions. On the other hand, the unverified controller is designed for functionality; i.e., it is expected to realize some tasks which are much more complicated than only keeping the system safe. To ensure a specific level of safety probability for the controlled physical system, the supervisor specifies verifiable safety rules for the unverified controller to follow. The control inputs of the controller fed to the system are checked at run-time and can only be accepted when they are not disobeying the safety rules defined in the sandboxing mechanism. In general, the Safe-visor architecture can be used to sandbox any types of unverified controllers in order to guarantee the safety of the controlled physical system. In summary, we are able to use an unverified controller for realizing complex tasks while preventing the system from being threatened by its harmful behavior, if any. Compared to existing literature that focuses on similar problems, a safe-visor architecture aims to provide safety guarantee while avoiding restricting too much the capability of unverified controller. From a security stand point, Safe-visor acts like a “last line of defence” mechanism by providing integrity and assurance, but it doesn’t preserve confidentiality. That means an intruder might still be able to observe the protected CPS, however, he/she cannot damage its physical components. Moreover, the proposed Safe-visor is complementary to, and works well with, existing methods that provide confidentiality, like encryption. Finally, this research focuses on stochastic Cyber-Physical Systems modeled as controlled Markov Process, which enables us to reason about the statistical properties of CPS influenced by sensor and input noise.
    As part of this research, we also want to look at more general safety specifications (e.g., those described by temporal logic) and more general system models (e.g., partial observable Markov Decision Process). Moreover, we want to develop software for CPS controllers’ synthesis and their code generation.


Integration of Artificial Intelligence (AI) with Factory Automation

    Industry automation currently relies on manually programmed algorithms as well as precise measurements and controls to actuate factory robots that are resource intensive to set up. They are not easily adaptable to product changes, and they do not offer the required level of flexibility to support new features like predictive mantainance, run-time quality control, real-time production monitoring, and more advanced features. Additionally, shorter product development cycles require novel approaches that can now be provided by AI. We aim to design AI-enhanced controllers for manufacturing robots to support flexible production lines and product changes without the need for extensive reprogramming and replanning.
    A promising research direction focuses on the integration of 6D pose detection into industrial production lines. 6D pose detection is the task of recognising an object’s position (i.e. translation) and orientation (i.e. rotation) in space. As part of this research, we are investigating how a 6D pose detection model can be safely and effectively integrated with the control software of an industrial robot. Such an integration would allow to guide the robot in real-time to precisely and safely pick up and move different objects within a manufacturing environment, without the need for reprogramming it.
    We aim at creating a training database for 6D pose recognition models. This is needed since there are just a few examples of databases labeled for this kind of problem (most notably LINEMOD), but those databases have limitations (e.g. only one object per image) and, more importantly, there is no quick way to expand those databases to train an AI algorithm for different datasets. As experimental testbed, we will use a prototype device from AISmart which uses a FANUC robot and a camera to automatically generate labelled datasets. The database will be public and will be shared with the scientific community.
    Industrial applicability: the proposed new database, which will include multiple objects and occlusions, will be used to test and enhance state of the art 6D pose models best suited for manufacturing precision and performance requirements. Finally, newly trained models will be tested in several “pick and place” scenarios typical of an industrial environment. Recently, 6D pose detection has seen significant progress in precision and performance reaching now 99% accuracy in real time (>20 FPS). Ultimately, we want to explore its industrial applications for production lines.

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