Communications

Currently autonomous robotic systems are designed for operation in environments defined by designers therefore suffering from limited knowledge of the designers which do not reflect the full spectrum of environments features and dynamics. The objective of the project is to create a framework for highly adaptive and long-term operation capable robotic system (including multi-robot) design using the Viable Systems Model as a core concept to achieve system’s adaptation. The framework will be based on a model which specifies the functions that are needed to implement adaptation to the changes in the environment and the system itself.  The fundamental breakthrough provided by the project will be the new level of adaptation and autonomy of robotic systems.

The convergence between the cyber and physical worlds generates new challenges for handling the information flowing between the cyber and physical worlds. Huge amount of information will be generated by several sources both in the physical (e.g., by environmental phenomena) and in the virtual world (by sensors, RFIDs, etc.), which need to be stored for future processing. In order to use this huge amount of heterogeneous data coming from a variety of sources, the quality of such data needs to be evaluated and taken into consideration before using it.

Provenance collection is an important component of enhancing the long-term preservation of digital data in the physical sciences and beyond. For the collections to have lasting value, however, and not all collections of data were intended to have lasting value, the data must have undergone some level of preservation. Usually the data pre-processing, such as data calibration, is fulfilled by external systems. Therefore, it is necessary to take into account the provenance information from both outside and inside the datasets. Besides, simply tracking what happened in history is far from enough. Tasks including maintaining lineage of data and the ability to use various versions of them should be involved as well.

Our provenance research has a major focus on instrumentation that minimizes perturbation on an application and minimizes the burden on the application programmer. The preservation step we focus on is automating the process of gathering of provenance about data. Data provenance is the lineage of a data product or collection of data and applies to observational and imagery data arriving in real time from cyber-physical systems, particularly from sensors, networks of sensors, and instruments; results from computational models and data mining; field studies such as documenting human use of a plot of land over time; regional positional data; scholarly reports in journals, etc. The larger context in which our research is carried out is enhancing the preservation record for the digital data coming from cyber-physical world. The drawback to minimized instrumentation and human burden costs is loss of provenance completeness or quality. This research focusses on trustworthy cyber-physical systems by putting emphasis on the Quality of Information by analyzing the Provenance Aspect.

In recent years, formal methods have proved to be important concepts for the testing, verification and validation of various kinds of computer systems. It is expected that important gains in terms of quality, safety and efficiency can be achieved through the formalization and automation of techniques such as trace analysis, runtime monitoring, behavioural specification and model-based testing.