Increasingly complex robotic platforms incorporate heterogeneous sensors and actuators. They are usually coupled with embedded computers but relying on software solutions not entirely suited for processing large amount of data concurrently and fast enough to keep real-time constrains. FPGAs are ideal candidates to enhance those systems computing capabilities while still being programmable. However, it is cumbersome to manually incorporate them into new or existing systems because providing accelerators with a specific integration capability limits their applicability. This work presents an approach to integrate multiple hardware accelerators into a robotic system. A model-based toolchain automatically generates the necessary hardware components (VHDL modules) from existing message specifications to exchange data with the accelerators. The goal is to seamlessly replace software components with FPGA-based ones while retaining the same communication interface. Instead of writing several hundred lines of VHDL, a dozen input specification lines are sufficient with our approach. The results are validated through an evaluation of all message specifications included in the latest Robot Operating System (ROS) versions. The 2295 messages evaluated show the robustness in our capabilities to support arbitrarily large ROS messages types, multiple data types, and nested messages. Moreover, our approach facilitates the extension from ROS1 to provide support for ROS2 easily. Finally, two use cases are shown to prove the feasibility of our approach on real applications. The first one incorporates a High Level Synthesis (HLS) hardware accelerator for image processing into an existing software architecture. The second one consists of a fully FPGA-based mobile platform with ROS features incorporated.

Modern robots are complex heterogeneous systems composed of different Processing Elements (PEs) with multiple sensors and actuators. This implies that different experts are needed to build such systems. Traditionally, robots included Central Processing Units (CPUs) as their PE. However, this has been changing over the last decade as different PEs, namely Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), have drawn the attention of roboticists. The research community focused on various techniques, methodologies, and applications separately, making integration aspects highly complex. Models, as abstractions, have been proposed to aid in designing complex systems that can also help with integration. Hence, three complementary goals are discussed in this work. The first is which robotic applications benefit from parallelizable and energy-efficient devices such as GPUs and FPGAs. The second one is to understand the contributions of different model-based approaches. Lastly, how these two can complement each other to bring benefits from one field onto the other so hardware developers, as well as roboticists, can improve the design of state-of-the-art robotic platforms.

Robotic systems compute data from multiple sensors to perform several actions (e.g., path planning, object detection). FPGA-based architectures for such systems may consist of several accelerators to process compute-intensive algorithms. Designing and implementing such complex systems tends to be an arduous task. This work proposes a modeling approach to generate architectures for such applications, compliant with existing robotics middlewares (e.g., ROS, ROS2). The challenge is to have a compact, yet expressive description of the system with just enough information to generate all required components and to integrate existing algorithms. The system model must be application-independent and leverage FPGA advantages, such as concurrency, energy efficiency, and acceleration due to custom designs, surpassing software-based solutions. Previous work mainly focused on individual accelerators rather than all components involved in a system and their interactions. The proposed approach exploits the advantages of model-driven engineering and model-based code generation to produce all components, i.e., message converters as middleware interfaces and wrappers to integrate algorithms. Data type and data flow analysis are performed to derive the necessary information to generate the components and their connections. Six different schedulers are proposed to cover multiple scenarios. Solutions to several identified challenges for generating entire systems from such models are evaluated using four different use cases.

From caretaking activities for elderly people to being assistive in healthcare setup, mobile and non-mobile robots have the potential to be highly applicable and serviceable. The ongoing pandemic has shown that human-to-human contact in healthcare institutions and senior homes must be limited. In this scenario, elderlies and immunocompromised individuals must be exclusively protected. Robots are a promising way to overcome this problem in assisted living environments. In addition, the advent of AI and machine learning will pave a way for intelligent robots with cognitive abilities, while enabling them to be more aware of their surroundings. In this paper, we discuss the general perspectives, potential research opportunities, and challenges arising in the area of robots in assisted living environments and present our research work pertaining to certain application scenarios, i.e., robots in rehabilitation and robots in hospital environments and pandemics, which, in turn, exhibits the growing prospects and interdisciplinary nature of the field of robots in assisted living environment.

Zusammenfassung: Im zukünftigen taktilen Internet wachsen die physische und virtuelle Welt auch über mehrere Standorte hinweg immer weiter zusammen. Robotic Co-working – das gemeinsame Kollaborieren von Robotern und Menschen – gewinnt dabei zunehmend im Kontext des Internet der Dinge (IoT) und cyber-physischer Systeme (CPS) an Bedeutung. Mit dieser Arbeit präsentieren wir eine Fallstudie, die anlässlich des 50-jährigen Jubiläums des Informatikstudiums in Deutschland durchgeführt wurde. In dieser arbeiten Menschen und Roboter über mehrere Standorte verteilt in einer virtuellen Co-Working-Zelle zusammen, um einen „physischen und virtuellen Informatik-Deutschland-Campus 2069“ zu konstruieren. Unter Nutzung von Sensorik, Aktuatorik und Software wird der cyber-physische Campus, von einem Workflow gesteuert, schrittweise errichtet. Mithilfe einer Mixed-Reality-Anwendung kann dieser Prozess immersiv, d. h. eintauchbar und interaktiv, unabhängig von einem konkreten Standort erlebt werden.