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.
Online trajectory generation approach that minimises uncertainty of a group of robots while maximising their efficiency in performing specific tasks.
Novel architecture to generate trajectories of a collaborative MRS.
Collaborative intelligence of MRS through the ability to share and compose experiences of individual robots with other robots.
2. Executable Scenarios and System Modelling
Scientific foundations, concepts and principles of the proposed Executable Scenario (ExSce) methodology for engineering dependable MRS.
Enables the storage and querying of provenance data for scenario executions.
Three tools conforming to the methodology of scenario-based development of multi-robot applications and construction of scenarios.
Metamodels and models developed to support the Executable Scenarios Workbench.
3. Safety-Targeted Executable Digital Dependability Identities
Safety analysis concept and methodology for developing safe and secure MRS integrating the key concepts 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 utilised to create appropriate system models and safety artefacts, which are 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.
Techniques and tools that have been adopted for securing the EDDIs and ensure no additional attack surfaces are created and no additional attack types are utilized.
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.
Tool-supported methodology for hardening and repairing DLs used for MRS that leverages the dependability assurance analysis results.
Digital twin technology that provides simulation-based tools with data from deployed MRS to perform failure prediction and almost real-time validation and verification of system adaptations.
Integrated methodology for the transition between simulation-based testing and
physical testing of MRS systems.
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.
Addresses how RunTime EDDIs can be designed and deployed as Multi-Agent Systems to collaborate towards MRS dependability assurance during operation.
Tools developed and upgraded for the purpose of explaining EDDI state and/or behaviour.