Adaptive optics real-time control with the compute and control for adaptive optics (Cacao) software framework
Author
Guyon, OlivierSevin, Arnaud
Ferreira, Florian
Ltaief, Hatem
Males, Jared R.
Deo, Vincent
Gratadour, Damien
Cetre, Sylvain
Martinache, Frantz
Lozi, Julien
Vievard, Sébastien
Fruitwala, Neelay
Bos, Steven P.
Skaf, Nour
Affiliation
Steward Observatory, University of ArizonaCollege of Optical Sciences, University of Arizona
Issue Date
2020-12-13
Metadata
Show full item recordPublisher
SPIECitation
Guyon, O., Sevin, A., Ferreira, F., Ltaief, H., Males, J., Deo, V., ... & Skaf, N. (2020, December). Adaptive optics real-time control with the compute and control for adaptive optics (Cacao) software framework. In Adaptive Optics Systems VII (Vol. 11448, p. 114482N). International Society for Optics and Photonics.Rights
© 2020 SPIE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The Compute and control for adaptive optics (Cacao) is an open source software package providing a flexible framework for deploying real-time adaptive optics control. Cacao leverages CPU and GPU computational resources to meet the demands of modern AO systems with thousands of degrees of freedom running at kHz speed or faster. Cacao adopts a modular approach, where individual processes operate over a standardized data stream stucture. Advanced control loops integrating multiple sensors and DMs are built by assembling multiple such processes. High-level constructs are provided for sensor fusion, where multiple sensors can drive a single physical DM. The common data stream format is at the heart of Cacao, holding data content in shared memory and timing information as semaphores. Cacao is currently in operation on the general-purpose Subaru AO188 system, the SCExAO and MagAOX extreme-AO instruments. Its data stream format has been adopted at Keck, within the COMPASS AO simulation tool, and in the COSMIC modular RTC platform. We describe Cacao's software architecture and toolset, and provide simple examples for users to build a real-time control loop. Advanced features are discussed, including on-sky results and experience with predictive control and sensor fusion. Future development plans will include leveraging machine learning algorithms for real-time PSF calibration and more optimal AO control, for which early on-sky demonstration will be presented. © 2020 SPIE.ISSN
0277-786XVersion
Final published versionae974a485f413a2113503eed53cd6c53
10.1117/12.2562822