The aim of the thematic dimension in WASP is to encapsulate the underlying scientific and technological challenges that are common to all types of autonomous systems, e.g., the need to learn and to collaborate. The thematic areas identified are a) Data Analytics and Learning, b) Collaboration and Interaction, c) Model-Based Systems Engineering, d) Networked and Distributed Systems, and e) Software for Engineering Design, Synthesis, and Autonomous Systems. Areas a, b, and d are focused on the capabilities required or implied by autonomy, whereas Areas c and e are focused on system development. Area c – Model-Based Systems Engineering – takes a model-based approach to the development of the entire system, including their software part, whereas Area e concentrates on software issues.
Data Analytics and Learning
Big Data provides a huge statistical sample that has to be turned into reliable information to be used for e.g. prediction, optimization, and automation. Analytics concerns the discovery of meaningful information in the data, while learning is the act of acquiring new, or modifying and reinforcing, existing knowledge, behaviours, and skills. Fusion of sensor information forms the base for e.g. localization, navigation and tracking.
Collaboration and Interaction
Safe collaboration and interaction between machines (vehicles, robots…) and humans is of fundamental importance for the societal acceptance of autonomous systems. To achieve this we need novel algorithms and supporting theory for designing and analysing efficient human-in-the-loop systems capable of solving complex missions.
Model-Based Systems Engineering
Model-Based Systems Engineering concerns the application of rigorous modelling principles, including requirements analysis and verification, functional analysis, and performance analysis for complex technical systems. This is a very broad field of research that combines best practice with advanced theory and software tools. Our focus in this broad field is on testing and verification extending formal methods with data-driven model verification algorithms.
Networked and Distributed Systems
Autonomy and intelligence are being embedded into a growing range of distributed physical systems linked together through communication networks. In order to manage security, complexity and flexibility of these systems, new theories and tools are needed that support the convergence of control, computing and communication in pervasive integration of smart, networked sensors and actuators into a connected world.
Software for Engineering Design, Synthesis, and Autonomous Systems
The Platform on Networked European Software and Services Initiative has identified software engineering research challenges in three major technology areas: Software engineering in and for the Cloud, Software engineering for Cyber-Physical Systems, and Software engineering for and with Big Data. Novel software engineering principles, techniques, methods and tools need to be developed in order to keep up with the fast technology advances in autonomous systems. Significant trends in this development include open innovation and software ecosystems.