The following early technical reports addressing Multi-Robot Systems (MRS) development and deployment technologies were completed during the first half of the project and have been made publicly available. Also available are published technical papers and journal articles by visiting our News section and clicking the Tag of interest. Click on any of the reports to download the document.
1. Sensor Fusion and Collaborative Intelligence
A novel concept of stratagem, which encapsulates the capabilities of individual robots and robotic teams.
Several model identification techniques are developed to be used at run-time by a robot to identify the dynamic and kinematic model of itself or other robots in MRS.
Perception and collaborative sensor fusion mechanisms that allow robots to comprehend their environment accurately.
2. Executable Scenarios and System Modelling
Scientific foundations, concepts and principles of the proposed Executable Scenario (ExSce) methodology for engineering dependable MRS.
Two initial tools conforming to the methodology of scenario-based development of multi-robot applications and construction of scenarios.
3. Safety-Targeted Executable Digital Dependability Identities
Safety analysis concept and methodology to be developed in SESAME and how the project intends to integrate key concepts as part of the EDDI.
Specification of the Open Dependability Exchange (ODE) meta model and the new safety and security modifications and extensions for MRS driven EDDIs.
A combined safety/security co-engineering framework based on the ODE, the metamodel that serves as a basis for the EDDI dependability management concept.
Existing safety analysis tools are targeted to create appropriate system models and safety artefacts, which can be converted to ODE-compliant models via tool adapters and model converters.
4. Security-Targeted Executable Digital Dependability Identities
Security assessment concept and methodology that will be used in the SESAME project.
Technologies and techniques for adapting EDDI and associated infrastructure to facilitate the deployment and integration of EDDIs to reduce effort and resource investment.
5. MRS-Executable Digital Dependability Identities Quality Assurance
Introduces DeepKnowledge providing a novel test adequacy criterion for testing DL-based systems and providing insights by analysing the generalisation behaviour of Deep Neural Networks (DNN) models under domain shift.
Methodology for simulation-based testing is presented based on the utilisation of a domain-specific language (DSL) to model the space of potential fuzz testing operations upon the MRS.
Details on deploying the simulation-based testing platform informed by the safety analysis that underlies the construction of the EDDIs, and the DeepKnowledge framework used to evaluate the quality of the EDDIs.
6. Runtime MRS Dependability Management
Address dependability of MRS through Executable Digital Dependability Identity (EDDI) runtime components.
Explains the toolchain for the generation of runtime EDDIs. Platform independent and dependent software components are semi-automatically generated, which contain functionality to dynamically supervise dependability properties of an MRS.