Abstract
Recently developed neuromorphic vision sensors have become promising candidates for agile and autonomous robotic applications primarily due to, in particular, their high temporal resolution and low latency. Each pixel of this sensor independently fires an asynchronous stream of 'retinal events' once a change in the light field is detected. Existing computer vision algorithms can only process periodic frames and so a new class of algorithms needs to be developed that can efficiently process these events for control tasks. In this paper, we investigate the problem of quadratically stabilizing a continuous-time linear time invariant (LTI) system using measurements from a neuromorphic sensor. We present an H∞ controller that stabilizes a continuous-time LTI system and provide the set of stabilizing neuromorphic sensor based cameras for the given system. The effectiveness of our approach is illustrated on an unstable system.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3030-3036 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509018376 |
| DOIs | |
| State | Published - Dec 27 2016 |
| Externally published | Yes |
| Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: Dec 12 2016 → Dec 14 2016 |
Other
| Other | 55th IEEE Conference on Decision and Control, CDC 2016 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 12/12/16 → 12/14/16 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Control and Optimization
Cite this
Stabilization of linear continuous-time systems using neuromorphic vision sensors. / Singh, Prince; Yong, Sze; Gregoire, Jean; Censi, Andrea; Frazzoli, Emilio.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3030-3036 7798722.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Stabilization of linear continuous-time systems using neuromorphic vision sensors
AU - Singh, Prince
AU - Yong, Sze
AU - Gregoire, Jean
AU - Censi, Andrea
AU - Frazzoli, Emilio
PY - 2016/12/27
Y1 - 2016/12/27
N2 - Recently developed neuromorphic vision sensors have become promising candidates for agile and autonomous robotic applications primarily due to, in particular, their high temporal resolution and low latency. Each pixel of this sensor independently fires an asynchronous stream of 'retinal events' once a change in the light field is detected. Existing computer vision algorithms can only process periodic frames and so a new class of algorithms needs to be developed that can efficiently process these events for control tasks. In this paper, we investigate the problem of quadratically stabilizing a continuous-time linear time invariant (LTI) system using measurements from a neuromorphic sensor. We present an H∞ controller that stabilizes a continuous-time LTI system and provide the set of stabilizing neuromorphic sensor based cameras for the given system. The effectiveness of our approach is illustrated on an unstable system.
AB - Recently developed neuromorphic vision sensors have become promising candidates for agile and autonomous robotic applications primarily due to, in particular, their high temporal resolution and low latency. Each pixel of this sensor independently fires an asynchronous stream of 'retinal events' once a change in the light field is detected. Existing computer vision algorithms can only process periodic frames and so a new class of algorithms needs to be developed that can efficiently process these events for control tasks. In this paper, we investigate the problem of quadratically stabilizing a continuous-time linear time invariant (LTI) system using measurements from a neuromorphic sensor. We present an H∞ controller that stabilizes a continuous-time LTI system and provide the set of stabilizing neuromorphic sensor based cameras for the given system. The effectiveness of our approach is illustrated on an unstable system.
UR - http://www.scopus.com/inward/record.url?scp=85010716421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010716421&partnerID=8YFLogxK
U2 - 10.1109/CDC.2016.7798722
DO - 10.1109/CDC.2016.7798722
M3 - Conference contribution
AN - SCOPUS:85010716421
SP - 3030
EP - 3036
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -