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CPS Design with Learning-Enabled Components: A Case Study

Published: 17 October 2019 Publication History

Abstract

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, leading 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 paper, 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.

References

[1]
M. Abadi et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2015.
[2]
Federal Aviation Administration. Unmanned Aircraft Systems (UAS) Operational Approval. online: faa.gov/documentLibrary/media/Notice/N_8900.227.pdf, 2013.
[3]
Torsten Blochwitz, Martin Otter, Martin Arnold, Constanze Bausch, H Elmqvist, A Junghanns, J Mauß, M Monteiro, T Neidhold, Dietmar Neumerkel, et al. The functional mockup interface for tool independent exchange of simulation models. In Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany, number 063, pages 105--114. Linköping University Electronic Press, 2011.
[4]
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
[5]
G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
[6]
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.
[7]
Brian Broll, Miklos Maroti, Peter Volgyesi, and Akos Ledeczi. DeepForge: A Scientific Gateway for Deep Learning. In Gateways 2018, 9 2018.
[8]
François Chollet. Keras. https://keras.io/, 2015.
[9]
D. Eastlake, 3rd and P. Jones. Us secure hash algorithm 1 (sha1). 2001.
[10]
Daniel J Fremont, T Dreossi, S Ghosh, X Yue, A L Sangiovanni-Vincentelli, and S A Seshia. Scenic: a language for scenario specification and scene generation. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 63--78. ACM, 2019.
[11]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
[12]
Patrick J Graydon and C Michael Holloway. An investigation of proposed techniques for quantifying confidence in assurance arguments. Safety science, 92:53--65, 2017.
[13]
C Hartsell, N Mahadevan, S Ramakrishna, A Dubey, T Bapty, T Johnson, X Koutsoukos, J Sztipanovits, and G Karsai. Model-based design for cps with learning-enabled components. In Proceedings of the Workshop on Design Automation for CPS and IoT, pages 1--9. ACM, 2019.
[14]
Simon Haykin. Neural networks: a comprehensive foundation. Prentice Hall PTR, 1994.
[15]
David Held, Sebastian Thrun, and Silvio Savarese. Learning to track at 100 fps with deep regression networks. In European Conference on Computer Vision, pages 749--765. Springer, 2016.
[16]
Tim Kelly and Rob Weaver. The goal structuring notation--a safety argument notation. In Proceedings of the dependable systems and networks 2004 workshop on assurance cases, page 6. Citeseer, 2004.
[17]
Nathan P Koenig and Andrew Howard. Design and use paradigms for gazebo, an open-source multi-robot simulator. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. Citeseer, 2004.
[18]
Jochen Köhler, Hans-Martin Heinkel, Pierre Mai, Jürgen Krasser, Markus Deppe, and Mikio Nagasawa. Modelica-association-project "system structure and parameterization"--early insights. In The First Japanese Modelica Conferences, May 23-24, Tokyo, Japan, number 124, pages 35--42. Linköping University Electronic Press, 2016.
[19]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012.
[20]
P. G. Larsen, J. Fitzgerald, J. Woodcock, P. Fritzson, J. Brauer, C. Kleijn, T. Lecomte, M. Pfeil, O. Green, S. Basagiannis, and A. Sadovykh. Integrated tool chain for model-based design of cyberphysical systems: The into-cps project. In 2016 2nd International Workshop on Modelling, Analysis, and Control of Complex CPS (CPS Data), pages 1--6, April 2016.
[21]
Musa Morena Marcusso Manhães, Sebastian A. Scherer, Martin Voss, Luiz Ricardo Douat, and Thomas Rauschenbach. UUV simulator: A gazebo-based package for underwater intervention and multi-robot simulation. In OCEANS 2016 MTS/IEEE Monterey. IEEE, sep 2016.
[22]
Miklós Maróti, Tamás Kecskés, Róbert Kereskényi, Brian Broll, Péter Völgyesi, László Jurácz, Tihamer Levendovszky, and Ákos Lédeczi. Next generation (meta) modeling: Web-and cloud-based collaborative tool infrastructure. MPM@ MoDELS, 1237:41--60, 2014.
[23]
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928--1937, 2016.
[24]
Vince Molnár, Bence Graics, András Vörös, István Majzik, and Dániel Varró. The gamma statechart composition framework. In Internation Conference on Software Engineering. ICSE, 2018.
[25]
UK Ministry of Defense. Safety management requirements for defence systems, June 2007.
[26]
OMG. OMG Systems Modeling Language (OMG SysML), Version 1.5, 2017.
[27]
Mete Ozay, Inaki Esnaola, Fatos Tunay Yarman Vural, Sanjeev R Kulkarni, and H Vincent Poor. Machine learning methods for attack detection in the smart grid. IEEE transactions on neural networks and learning systems, 27(8):1773--1786, 2016.
[28]
Morgan Quigley, Ken Conley, Brian P. Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y. Ng. Ros: an open-source robot operating system. In ICRA Workshop on Open Source Software, 2009.
[29]
Viktor Rausch, A Hansen, E Solowjow, C Liu, E Kreuzer, and J K Hedrick. Learning a deep neural net policy for end-to-end control of autonomous vehicles. In 2017 American Control Conference (ACC), pages 4914--4919. IEEE, 2017.
[30]
RTCA. DO-178C - Software Considerations in Airborne Systems and Equipment Certification. December 2011.
[31]
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. Hidden technical debt in machine learning systems. In NIPS, 2015.
[32]
Sanjit A. Seshia and Dorsa Sadigh. Towards verified artificial intelligence. CoRR, abs/1606.08514, 2016.
[33]
S. M. Sombolestan, A. Rasooli, and S. Khodaygan. Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning. Journal of Ambient Intelligence and Humanized Computing, Mar 2018.
[34]
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[35]
Clemens Szyperski. Component Software: Beyond Object-Oriented Programming. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition, 2002.
[36]
András Vörös, Márton Búr, István Ráth, Ákos Horváth, Zoltán Micskei, László Balogh, Bálint Hegyi, Benedek Horváth, Zsolt Mázló, and Dániel Varró. Modes3: model-based demonstrator for smart and safe cyberphysical systems. In NASA Formal Methods Symposium, pages 460--467. Springer, 2018.
[37]
Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel A. Rosenfeld, and Taylor T. Johnson. Verification for machine learning, autonomy, and neural networks survey. CoRR, abs/1810.01989, 2018.

Cited By

View all
  • (2024)Underwater Simulators Analysis for Digital TwinningIEEE Access10.1109/ACCESS.2024.337044312(34306-34324)Online publication date: 2024
  • (2023)Designing Reconfigurable Cyber-Physical Systems Using Unified Modeling LanguageEnergies10.3390/en1603127316:3(1273)Online publication date: 25-Jan-2023
  • (2022)A Mapping of Assurance Techniques for Learning Enabled Autonomous Systems to the Systems Engineering Lifecycle2022 IEEE International Conference on Assured Autonomy (ICAA)10.1109/ICAA52185.2022.00013(28-35)Online publication date: Mar-2022
  • Show More Cited By

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Reviews

Pierre N. Radulescu-Banu

Cyber-physical systems (CPSs) integrate hardware/software components with mechanical/electronic equipment to operate in applications for robotics, avionics, smart grids, and the like. This paper presents a case study for the design of a CPS to control the movement of an autonomous unmanned underwater vehicle (UUV). The UUV tracks "a pipe placed on the seafloor using images from a forward-looking camera." Several aspects need to be considered. The first issue is the innate degree of uncertainty due to the complex interactions inside the system and between the system and the environment. The traditional control technologies show their limits and are replaced by CPSs based on learning-enabled components (LECs). Second, the security-critical nature of these applications: they must react correctly even to events that happen only rarely. The design must comply with certification processes, requiring safety assurance arguments backed by substantial evidence. Third, all this complexity requires a framework supporting environment simulation and also testing in the earlier stages, including on the software models. The authors use an assurance-based learning-enabled CPS (ALC) toolchain as the development framework. Development starts with the modeling of components and messages. ALC utilizes three tools to get the whole architectonic model: the SysML language to define the components as blocks, the robot operating system (ROS) for inter-component communication, and the WebGME infrastructure for instantiating the blocks. Any time the original blocks are modified, ALC updates their instantiations automatically. Each block can have various implementation solutions. All these implementations are evaluated to get the optimum. The LEC construction follows. ALC allows developers to insert code into blocks. The UUV application uses Python. Data is generated using the Gazebo environment simulator (the authors are currently integrating the SCENIC language for data generation). ALC supports training through artificial neural networks and supervised learning. The goal is to "approximate the ideal mapping function from a set of input variables to a corresponding set of output variables." The whole process is iterative. The paper presents the development cycle in detail and evaluates the results obtained for diverse changes of conditions (various architectures for the neural networks, various geometries of the pipeline, and so on). The concluding section identifies possible avenues for future research related to the "formalization and quantitative evaluation of safety case arguments." The authors are at Vanderbilt University. The paper level is industry/academia.

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Published In

cover image ACM Conferences
RSP '19: Proceedings of the 30th International Workshop on Rapid System Prototyping (RSP'19)
October 2019
80 pages
ISBN:9781450368476
DOI:10.1145/3339985
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 October 2019

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Author Tags

  1. cyber physical systems
  2. machine learning
  3. model based design

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • AFRL/DARPA

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ESWEEK '19
ESWEEK '19: Fifteenth Embedded Systems Week
October 17 - 18, 2019
NY, New York, USA

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Cited By

View all
  • (2024)Underwater Simulators Analysis for Digital TwinningIEEE Access10.1109/ACCESS.2024.337044312(34306-34324)Online publication date: 2024
  • (2023)Designing Reconfigurable Cyber-Physical Systems Using Unified Modeling LanguageEnergies10.3390/en1603127316:3(1273)Online publication date: 25-Jan-2023
  • (2022)A Mapping of Assurance Techniques for Learning Enabled Autonomous Systems to the Systems Engineering Lifecycle2022 IEEE International Conference on Assured Autonomy (ICAA)10.1109/ICAA52185.2022.00013(28-35)Online publication date: Mar-2022
  • (2021)Fault-Adaptive Autonomy in Systems with Learning-Enabled ComponentsSensors10.3390/s2118608921:18(6089)Online publication date: 11-Sep-2021
  • (2021)Assuring Learning-Enabled Components in Small Unmanned Aircraft SystemsAIAA Scitech 2021 Forum10.2514/6.2021-0994Online publication date: 4-Jan-2021
  • (2021)A software engineering perspective on engineering machine learning systems: State of the art and challengesJournal of Systems and Software10.1016/j.jss.2021.111031(111031)Online publication date: Jun-2021

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