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RIS citation export for WEAL02: A Framework for High Level Machine Automation Based on Behavior Tree

AU  - Gaio, G.
AU  - Cinquegrana, P.
AU  - Krecic, S.
AU  - Scalamera, G.
AU  - Strangolino, G.
AU  - Tripaldi, F.
AU  - Trovò, M.
AU  - Zambon, L.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - A Framework for High Level Machine Automation Based on Behavior Tree
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 534
EP  - 539
KW  - controls
KW  - database
KW  - operation
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEAL02
UR  - https://jacow.org/icalepcs2021/papers/weal02.pdf
ER  -