ALC Toolchain
ALC Toolchain

The Assurance-based Learning-enabled Cyber-Physical Systems (ALC) toolchain is an integrated set of tools and corresponding workflows specifically tailored for the model-based development of Cyber-Physical Systems (CPS) that utilize Learning-Enabled Components (or LECs). 

ALC toolchain supports:

  • Modeling:  A WebGME based modeling environment that supports SysML-style models and their extensions that include architecture models, requirements model, functional decomposition models, dynamic risk models and hazard models as well as cross-linking these models to capture their inter-dependencies. 

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    BlueROV  System Architecture Modeling
    BlueROV System Architecture Modeling
  • Execution: extensive support for model construction, engineering, and integration of LECs and assurance technologies, including: 

    • “Headless” execution of simulations for training data collection, LEC training, evaluation, and verification.

    • Setup and execution of campaigns over parameter spaces of interest.

    • Orchestration of workflows related to development, testing, and evaluation of LECs and related assurance technologies.

    • IDE/VNC/ git/Docker:  web based Integrated Development Environment (IDE) for interactive code development; debugging and testing of the software components including support for viewing graphical user interfaces using web based VNC; git server to host software repositories for the version control of software components; docker registry to store and share the images between the interactive environment and the headless execution.

    • Support for extension points to integrate new simulation environments, associated software tools for scenario/ data generation, deployment, and testing.

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    vnc
    VNC enabled remote visualization of running simulation. 
  • Assurance technologies: 

    • Dynamic run-time assurance monitors that can detect, at run-time, Out-of-Distribution anomalies of the inputs (and/or output/s) of the LECs.

    • ReSonAte based computation of metrics related to static and dynamic assurance metrics for hazard mitigation in the threat propagation paths captured in the dynamic risk evaluation models.

    • Real-time reachability analysis to predict safe and unsafe operation of the LECs  in operating environments.

    • Behavioral-tree based support for contingency management for mission execution.

  • Assurance cases

    • modeling and analysis of safety cases with Goal Structuring Notations (GSN) and their cross-referencing to SysML models and evidences for explanation and traceability.

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Integrated browser accessible code editor
Integrated browser accessible code editor

The toolchain includes a BlueROV2 based UUV demonstration platform to highlight the above technologies. 

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Links
Acknowledgements

This work is supported in part by the  DARPA Assured Autonomy  program.

Contacts
ORGANIZATION

Vanderbilt University, Nashville, TN, USA

Contributors

Nagabhushan Mahadevan

Charles Hartsell

Shreyas Ramakrishna

Daniel Stojcsics

Abhishek Dubey

Ted Bapty

Harmon Nine