Micro Factory: Concept d’une chaîne d’assemblage miniature, modulaire et propre

La manipulation et l’assemblage de microsystèmes (MEMS) nécessite des robots de haute précision qui doivent fonctionner dans des environnements propres. Beaucoup des systèmes actuels ont un volume important et occupent une grande surface au sol par rapport à la taille des pièces fabriquées. Le but du projet « Micro-Factory » est de démontrer la faisabilité d’une chaîne de production miniature et modulaire et de développer une méthodologie de conception de telles salles blanches miniatures et modulaires pour des microsystèmes. Cette méthodologie permettra de définir rapidement la taille et la configuration de microfactory qui seront optimales pour chaque produit

PocketFactory : a modular and miniature assembly chain including a clean environment

The manipulation and assembly of microsystems (MEMS) require accurate robots operating in a clean room environment. Available systems require usually large ground surface for small components and are bulky. Furthermore it is difficult to move it to another place. The main objective of the 'Pocket-factory' project is to develop miniaturized and modular clean production units for microsystems and establish a data base of needs and available accessories to quickly set up a production chain

“Pocket Factory”: Concept of miniaturized modular cleanrooms

The manipulation and assembly of microsystems (MEMS) require accurate robots operating in a clean room environment. Available systems are bulky, thus requiring large ground surface for small components. Furthermore they have limited accuracy. The main objective of the 'Pocket-factory' project is to develop miniaturized and modular clean production units for microsystems and establish a methodology to quickly set them u

Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system

[EN] Rehabilitation is a hazardous task for a mechanical system, since the device has to interact with the human extremities without the hands-on experience the physiotherapist acquires over time. A gap needs to be filled in terms of designing effective controllers for this type of devices. In this respect, the paper describes the design of a model-based control for an electromechanical lower-limb rehabilitation system based on a parallel kinematic mechanism. A controller-observer was designed for estimating joint velocities, which are then used in a hybrid position/force control scheme. The model parameters are identified by customising an approach based on identifying only the relevant system dynamics parameters. Findings obtained through simulations show evidence of improvement in tracking performance compared with those where the velocity was estimated by numerical differentiation. The controller is also implemented in an actual electromechanical system for lower-limb rehabilitation tasks. Findings based on rehabilitation tasks confirm the findings from simulations.This work was partially financed by the Plan Nacional de I+D, Comision Interministerial de Ciencia y Tecnologia (FEDERCICYT) under the project DPI2013-44227-R and by the Instituto U. de Automatica e Informatica Industrial (ai2) of the Universitat Politecnica de Valencia.Valera Fernández, Á.; Díaz-Rodríguez, M.; Vallés Miquel, M.; Oliver, E.; Mata Amela, V.; Page Del Pozo, AF. (2017). Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system. International Journal of Control. 90(4):702-714. https://doi.org/10.1080/00207179.2016.1215529S702714904Åström, K. J., & Murray, R. M. (2010). Feedback Systems. doi:10.2307/j.ctvcm4gdkAtkeson, C. G., An, C. H., & Hollerbach, J. M. (1986). 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A revised Durbin-Wu-Hausman test for industrial robot identification. Control Engineering Practice, 48, 52-62. doi:10.1016/j.conengprac.2015.12.017Jiménez-Fabián, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering & Physics, 34(4), 397-408. doi:10.1016/j.medengphy.2011.11.018Khalil, W., Vijayalingam, A., Khomutenko, B., Mukhanov, I., Lemoine, P., & Ecorchard, G. (2014). OpenSYMORO: An open-source software package for symbolic modelling of robots. 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. doi:10.1109/aim.2014.6878246Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009). Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation, 6(1). doi:10.1186/1743-0003-6-20Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., & Xie, S. (Shane). (2015). 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Differential game theory for versatile physical human-robot interaction

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other’s control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies