Co-Design of Anytime Computation and Robust Control
Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam

Citation
Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X. Nghiem, Joseph Devietti, Rahul Mangharam. "Co-Design of Anytime Computation and Robust Control". 2015 IEEE Real-Time Systems Symposium (RTSS), December 2015.

Abstract
Control software of autonomous robots has stringent real-time requirements that must be met to achieve the control objectives. One source of variability in the performance of a control system is the execution time and accuracy of the state estimator that provides the controller with state information. This estimator is typically perception-based (e.g., Computer Vision-based) and is computationally expensive. When the computational resources of the hardware platform become overloaded, the estimation delay can compromise control performance and even stability. In this paper, we define a framework for co-designing anytime estimation and control algorithms, in a manner that accounts for implementation issues like delays and inaccuracies. We construct an anytime perception-based estimator from standard off-the-shelf Computer Vision algorithms, and show how to obtain a trade-off curve for its delay vs estimate error behavior. We use this anytime estimator in a controller that can use this trade- off curve at runtime to achieve its control objectives at a reduced energy cost. When the estimation delay is too large for correct operation, we provide an optimal manner in which the controller can use this curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing basic objectives are met. We illustrate our approach on an autonomous hexrotor and demonstrate its advantage over a system that does not exploit co-design.

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  • HTML
    Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X.
    Nghiem, Joseph Devietti, Rahul Mangharam. <a
    href="http://www.terraswarm.org/pubs/591.html"
    >Co-Design of Anytime Computation and Robust
    Control</a>, <i>2015 IEEE Real-Time Systems
    Symposium (RTSS)</i>, December 2015.
  • Plain text
    Yash Vardhan Pant, Kartik Mohta, Houssam Abbas, Truong X.
    Nghiem, Joseph Devietti, Rahul Mangharam. "Co-Design of
    Anytime Computation and Robust Control". <i>2015
    IEEE Real-Time Systems Symposium (RTSS)</i>, December
    2015.
  • BibTeX
    @article{PantMohtaAbbasNghiemDeviettiMangharam15_CoDesignOfAnytimeComputationRobustControl,
        author = {Yash Vardhan Pant and Kartik Mohta and Houssam
                  Abbas and Truong X. Nghiem and Joseph Devietti and
                  Rahul Mangharam},
        title = {Co-Design of Anytime Computation and Robust Control},
        journal = {2015 IEEE Real-Time Systems Symposium (RTSS)},
        month = {December},
        year = {2015},
        abstract = {Control software of autonomous robots has
                  stringent real-time requirements that must be met
                  to achieve the control objectives. One source of
                  variability in the performance of a control system
                  is the execution time and accuracy of the state
                  estimator that provides the controller with state
                  information. This estimator is typically
                  perception-based (e.g., Computer Vision-based) and
                  is computationally expensive. When the
                  computational resources of the hardware platform
                  become overloaded, the estimation delay can
                  compromise control performance and even stability.
                  In this paper, we define a framework for
                  co-designing anytime estimation and control
                  algorithms, in a manner that accounts for
                  implementation issues like delays and
                  inaccuracies. We construct an anytime
                  perception-based estimator from standard
                  off-the-shelf Computer Vision algorithms, and show
                  how to obtain a trade-off curve for its delay vs
                  estimate error behavior. We use this anytime
                  estimator in a controller that can use this trade-
                  off curve at runtime to achieve its control
                  objectives at a reduced energy cost. When the
                  estimation delay is too large for correct
                  operation, we provide an optimal manner in which
                  the controller can use this curve to reduce
                  estimation delay at the cost of higher inaccuracy,
                  all the while guaranteeing basic objectives are
                  met. We illustrate our approach on an autonomous
                  hexrotor and demonstrate its advantage over a
                  system that does not exploit co-design.},
        URL = {http://terraswarm.org/pubs/591.html}
    }
    

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