Author: Gaio, G.
Paper Title Page
WEAL02 A Framework for High Level Machine Automation Based on Behavior Tree 534
 
  • G. Gaio, P. Cinquegrana, S. Krecic, G. Scalamera, G. Strangolino, F. Tripaldi, M. Trovò, L. Zambon
    Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
 
  In order to carry out complex tasks on particle accelerators, physicists and operators need to know the correct sequence of actions usually performed through a large number of graphical panels. The automation logics often embedded in the GUIs prevents its reuse by other programs, thus limiting the level of automation a control system can achieve. In order to overcome this limitation we have introduced a new automation framework for shifting the logics from GUIs to server side, where simple tasks can be easily organized, inspected and stacked up to build more complex actions. This tool is based on Behavior Trees (BT) which has been recently adopted in the gaming industry for in-game AI player opponents. They are able to create very complex tasks composed by simple decoupled self-contained tasks (nodes), regardless how they are implemented. The automation framework has been deployed in the Elettra and FERMI TANGO-based control systems to implement autonomous operations. A dedicated Qt GUI and a web interface allow to inspect the BTs and dynamically go through a tree, visualize the dependencies, monitor the execution and display any running action.  
slides icon Slides WEAL02 [1.809 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEAL02  
About • Received ※ 08 October 2021       Revised ※ 18 October 2021       Accepted ※ 21 November 2021       Issue date ※ 08 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPV008 Online Automatic Optimization of the Elettra Synchrotron 636
 
  • G. Gaio, S. Krecic, F. Tripaldi
    Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
 
  Online automatic optimization is a common practice in particle accelerators. Beside the tryouts based on Machine Learning, which are effective especially on non-linear systems and images but are very complex to tune and manage, one of the most simple and robust algorithms, the simplex Nelder Mead, is extensively used at Elettra to automatically optimize the synchrotron parameters. It is currently applied to optimize the efficiency of the booster injector by tuning the pre-injector energy, the trajectory and optics of the transfer lines, and the injection system of the storage ring. It has also been applied to maximize the intensity of the photon beam on a beamline by changing the electron beam position and angle inside the undulator. The optimization algorithm has been embedded in a TANGO device that also implements generic and configurable multi-input multi-output feedback systems. This optimization tool is usually included in a high level automation framework based on behavior trees in charge of the whole process of machine preparation for the experiments.  
poster icon Poster WEPV008 [1.600 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV008  
About • Received ※ 08 October 2021       Accepted ※ 26 January 2022       Issue date ※ 25 February 2022  
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)