Assurance-based Learning-enabled Cyber-Physical Systems

Cyber-Physical Systems (CPS) are used in many applications where they must perform complex tasks with a high degree of autonomy in uncertain environments. Traditional design flows based on domain knowledge and analytical models are often impractical for tasks such as perception, planning in uncertain environments, control with ill-defined objectives, etc. Machine learning based techniques have demonstrated good performance for such difficult tasks, lead- ing to the introduction of Learning-Enabled Components (LEC) in CPS. Model based design techniques have been successful in the development of traditional CPS, and toolchains which apply these techniques to CPS with LECs are being actively developed. As LECs are critically dependent on training and data, one of the key challenges is to build design automation for them. In this pa- per, we examine the development of an autonomous Unmanned Underwater Vehicle (UUV) using the Assurance-based Learning- enabled Cyber-physical systems (ALC) Toolchain. Each stage of the development cycle is described including architectural modeling, data collection, LEC training, LEC evaluation and verification, and system-level assurance.

Citation: Charles Hartsell, Nagabhushan Mahadevan, Shreyas Ramakrishna, Abhishek Dubey, Theodore Bapty, Taylor Johnson, Xenofon Koutsoukos, Janos Sztipanovits, and Gabor Karsai. 2019. CPS Design with Learning-Enabled Components: A Case Study. In Proceedings of the 30th International Workshop on Rapid System Prototyping (RSP'19) (RSP '19). ACM, New York, NY, USA, 57-63. DOI: https://doi.org/10.1145/3339985.3358491