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Inferring and monitoring invariants in robotic systems

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Abstract

System monitoring can help to detect anomalies, but crafting monitors for robot systems is difficult due to the inherent complexity, changing, and uncertain operating environment. We address this challenge by automatically inferring system invariants and synthesizing those invariants into monitors to detect faults with an approach inspired by state of the art software engineering methods. Our approach is novel in that: (1) It automatically derives invariants from messages; (2) The invariants types are tailored to match the spatial, temporal, and architectural attributes of robotic systems; and (3) It automatically classifies and synthesizes invariants into an online invariants monitor node. We have assessed the approach in the context of two unmanned aerial vehicle systems running robot operating system. We found that monitoring the inferred invariants can reduce system failure rates when facing unexpected contexts from 76 to 11 %, and can detect differences between the lab environment and the field deployments.

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Notes

  1. Robot Operating System (ROS), http://www.ros.org.

  2. Lightweight Communications and Marshalling (LCM), https://code.google.com/p/lcm/.

  3. Microsoft Robotics Developer Studio http://msdn.microsoft.com/en-us/robotics/.

  4. CLARAty Robotic Software, https://claraty.jpl.nasa.gov.

  5. The daikon invariant detector, http://groups.csail.mit.edu/pag/daikon/.

  6. ROS Bag utility, http://wiki.ros.org/Bags.

  7. The cost of generating invariants with more than two variables was exponential and hence prohibitive unless it was focused on a small set of topics.

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Acknowledgments

This work was partially supported by Air Force Office of Scientific Research #FA9550-10-1-0406, United States Department of Agriculture National Institute of Food and Agriculture #2013-67021-20947, and National Science Foundation CSR-1217400. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these agencies.

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Authors and Affiliations

  1. Nebraska Intelligent MoBile Unmanned Systems (NIMBUS) Lab, Department of Computer Science and Engineering, University of Nebraska-Lincoln, 220 Schorr Center, Lincoln, NE, 68588, USA

    Hengle Jiang, Sebastian Elbaum & Carrick Detweiler

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  1. Hengle Jiang
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  2. Sebastian Elbaum
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  3. Carrick Detweiler
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Correspondence to Carrick Detweiler.

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Jiang, H., Elbaum, S. & Detweiler, C. Inferring and monitoring invariants in robotic systems. Auton Robot 41, 1027–1046 (2017). https://doi.org/10.1007/s10514-016-9576-y

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