TY - GEN
T1 - Stabilization of stochastic linear continuous-time systems using noisy neuromorphic vision sensors
AU - Singh, Prince
AU - Yong, Sze
AU - Frazzoli, Emilio
N1 - Funding Information:
This work was done at the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology (MIT) and was supported by the Singapore National Research Foundation through the SMART Future Urban Mobility project.
Publisher Copyright:
© 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - Vision-based robotic applications with aggressive maneuvers suffer from the low sensing speed of standard cameras that sample frames at constant time intervals. On the other hand, although neuromorphic vision sensors are promising candidates to provide the needed high-frequency sensing, a new class of algorithms needs to be synthesized that can deal with the uncommon output from each pixel of these sensors, which (independently of other pixels) fire an asynchronous stream of 'retinal events' once a change in the light field is detected. In this paper, we investigate the problem of stabilizing a stochastic continuous-time linear time invariant system using noisy measurements from a neuromorphic vision sensor. We propose an H∞ controller that addresses this problem and provide the critical event-generation threshold for these neuromorphic vision sensors and characterize the statistical properties of the resulting states. The efficacy of our approach is illustrated on an unstable system.
AB - Vision-based robotic applications with aggressive maneuvers suffer from the low sensing speed of standard cameras that sample frames at constant time intervals. On the other hand, although neuromorphic vision sensors are promising candidates to provide the needed high-frequency sensing, a new class of algorithms needs to be synthesized that can deal with the uncommon output from each pixel of these sensors, which (independently of other pixels) fire an asynchronous stream of 'retinal events' once a change in the light field is detected. In this paper, we investigate the problem of stabilizing a stochastic continuous-time linear time invariant system using noisy measurements from a neuromorphic vision sensor. We propose an H∞ controller that addresses this problem and provide the critical event-generation threshold for these neuromorphic vision sensors and characterize the statistical properties of the resulting states. The efficacy of our approach is illustrated on an unstable system.
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U2 - 10.23919/ACC.2017.7963008
DO - 10.23919/ACC.2017.7963008
M3 - Conference contribution
AN - SCOPUS:85027063846
T3 - Proceedings of the American Control Conference
SP - 535
EP - 541
BT - 2017 American Control Conference, ACC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 American Control Conference, ACC 2017
Y2 - 24 May 2017 through 26 May 2017
ER -