GAMHE

Group of advanced Automation of Machines, Highly complex processes and Environments

Authors: Beruvides, G; Juanes, C; Castaño, F; Haber, R.

Conference: IEEE International Conference on Industrial Informatics – INDIN15, Cambridge, UK pp. 1180-1185., July 22-24th, 2015

Participant: Antonio Artuñedo

Conference: IEEE 18th International Conference on Intelligent Transportation Systems, September 15-18th, 2015, Las Palmas de Gran Canaria, Spain.

GAMHE researchers working in ACCUS european project will attend the next General Assembly.

Participant: Jorge Godoy

Conference: XV Spanish Congress on Intelligent Transport Systems, April 14-16, 2015, Madrid, Spain.

Participant: Fernando Castaño

Conference: The 40th Annual Conference of the IEEE Industrial Electronics Society (IECON 2014), October 29 - November 1, 2014, Dallas, Texas, USA.

Participant: Gerardo Beruvides

Conference: IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), November 10-12, 2014, Limassol, Cyprus.

Participant: Rodolfo Haber

Conference: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), December 9-12, 2014, Orlando, Florida, USA.

Participant: Rodolfo Haber

Conference: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), December 9-12, 2014, Orlando, Florida, USA.

The last decades have shown impressive results emerging due to faster and cheaper computation and bandwidth.

GAMHE is a research group of the Centre for Automation and Robotics (CAR), CSIC-UPM. Founded in 1981, GAMHE has a long tradition in advanced research in intelligent automation, mainly for manufacturing processes.

Design and implementation of self-x capabilities in an artificial cognitive architecture

Relying on the experimental results we have demonstrated that the artificial cognitive control yields good results and promising opportunities to deal with complex systems even running in a low power machine.

Design and implementation of self-x capabilities in an artificial cognitive architecture

The main objective of this work is the development of computational technologies and algorithms in order to enable faster, self-organized, self-optimized behavior of micromanufacturing processes by means of intelligent control systems.