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© Thomson Reuters, Journal Citation Reports, 2016

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  • Factor de Impacto: 0,500(2016)
  • 5-años Factor de Impacto: 0,344
  • SCImago Journal Rank (SJR):0,212
  • Source Normalized Impact per Paper (SNIP):0,308

© Thomson Reuters, Journal Citation Reports, 2016

Revista Iberoamericana de Automática e Informática industrial 2017;14:193-204 - DOI: 10.1016/j.riai.2016.09.011
Heurísticas para el Ajuste de Algoritmos de Control de Plataformas Robóticas de Movimiento en Simuladores
Heuristics for solving the parameter tuning problem in motion cueing algorithms
Sergio Casas1, , Cristina Portalés, , José V. Riera, Marcos Fernández
Instituto de Robótica y Tecnologías de la Información y la Comunicación, Universitat de València, C/ Catedrático José Beltrán 2, 46980, Paterna, Valencia, España
Resumen

Diversos tipos de plataformas robóticas son empleadas habitualmente para la generación de claves gravito-inerciales en simuladores. Además del control de los actuadores, dichas plataformas deben ejecutar complejos algoritmos de control conocidos como algoritmos de washout, que deben ser ajustados para que el movimiento generado sea similar al simulado. El ajuste de dichos algoritmos es complejo por el elevado número de parámetros que poseen. Además, dicho ajuste se ha venido realizando tradicionalmente de modo manual mediante evaluaciones subjetivas. En este trabajo, los autores proponen un método automático de ajuste basado en optimización heurística, métricas objetivas, y simulación de la plataforma robótica para conseguir realizar el ajuste de manera más rápida. Se valida la corrección de las soluciones, y se comparan diversas técnicas de optimización, para concluir que la técnica más apropiada es la de los algoritmos genéticos.

Abstract

Motion cueing algorithms are commonly used in vehicle simulators to control robotic motion platforms. These algorithms usually have a significant number of parameters that need to be tuned. This process has been traditionally performed in a pilot-in-the-loop subjective manner. The authors propose a simulation-based objective and automatic method using heuristic optimization. Several schemes are proposed, assessed and compared in this paper, showing that a genetic algorithm is the one that suits best this problem.

Palabras clave
Plataformas de movimiento, heurísticas, simuladores, algoritmos de control, ajuste de parámetros, robótica, optimización
Keywords
Motion platforms, heuristics, simulation, motion cueing algorithms, tuning, robotics, optimization
Referencias
Alba et al., 2009
E. Alba,C. Blum,P. Asasi,C. Leon,J.A. Gomez
Optimization techniques for solving complex problems
Wiley, (2009)
Bertsimas and Tsitsiklis, 1993
D. Bertsimas,J. Tsitsiklis
Simulated Annealing
Statistical Science, 9 (1993), pp. 10-15
Casas et al., 2014
S. Casas,J.M. Alcaraz,R. Olanda,I. Coma,M. Fernández
Towards an extensible simulator of real motion platforms
Simulation Modelling Practice and Theory, 45 (2014), pp. 50-61
Casas et al., 2015
S. Casas,I. Coma,J.V. Riera,M. Fernández
Motion-Cuing Algorithms: Characterization of Users’ Perception
Human Factors: The Journal of the Human Factors and Ergonomics Society, 57 (2015), pp. 144-162
Casas et al., 2012a
S. Casas,R. Olanda,M. Fernandez,J.V. Riera
A faster than real-time simulator of motion platforms
CMMSE, (2012)
Casas et al., 2012b
S. Casas,S. Rueda,J.V. Riera,M. Fernández
On the Real-time Physics Simulation of a Speed-boat Motion
GRAPP/IVAPP, (2012)
Colombet et al., 2008
F. Colombet,M. Dagdelen,G. Reymond,C. Pere,F. Merienne,A. Kemeny
Motion Cueing: what's the impact on the driver's behaviour? Driving Simulator Conference
Monte-Carlo, (2008)
Cossalter et al., 2011
V. Cossalter,R. Lot,M. Massaro,R. Sartori
Development and Validation of an Advanced Motorcycle Riding Simulator
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 225 (2011), pp. 705-720
Díaz et al., 1996
A. Díaz,F. Glover,H.M. Ghaziri,J.L. González,M. Laguna,P. Moscato,F.T. Tseng
Optimización Heurística y Redes Neuronales
Paraninfo, (1996)
Edelkamp and Schroedl, 2011
S. Edelkamp,S. Schroedl
Heuristic Search: Theory and Applications
Morgan Kaufman - Elsevier, (2011)
Ferri et al., 1998
F.J. Ferri,J.V. Albert,G. Martín
Introducció a l’Anàlisi i Disseny d’Algorismes
Publicacions de la Universitat de València, (1998)
Garrett and Best, 2010
Garrett, N. J. I., Best, M. C., 2010. Driving simulator motion cueing algorithms – a survey of the state of the art. Proceedings of the 10th International Symposium on Advanced Vehicle Control (AVEC), Loughborough, UK.
Go et al., 2003
Go, T. H., Bürki-Cohen, J., Chung, W. H., Schroeder, J. A., Saillant, G., Jacobs, S., Longridge, T., 2003. The Effects of Enhanced Hexapod Motion on Airline Pilot Recurring Training and Evaluation AIAA Modeling and Simulation Technologies Conference and Exhibit, Austin, TX, USA.
Grant, 1996
P.R. Grant
The Development of a Tuning Paradigm for Flight Simulator Motion Drive Algorithms
PhD, University of Toronto, (1996)
Grant and Reid, 1997
P.R. Grant,L.D. Reid
Motion Washout Filter Tuning: Rules and Requirements
Journal of Aircraft, 34 (1997), pp. 145-151
Gutridge, 2004
Gutridge, J., 2004. Three Degree-of-Freedom Simulator Motion Cueing Using Classical Washout Filters and Acceleration Feedback. Master Thesis, Virginia Polytechnic Institute & State University.
Hammersley and Handscomb, 1964
Hammersley, J. M., Handscomb, D. C., 1964. Monte Carlo Methods. London, UK & New York, NY, USA.
Holland, 1992
J.H. Holland
Genetic Algorithms
Scientific American, 267 (1992), pp. 66-72
Kelley, 1995
D. Kelley
Automata and Formal Languages: An Introduction
Prentice Hall, (1995)
Korobeynikov and Turlapov, 2005
Korobeynikov, A. V., Turlapov, V. E., 2005. Modeling and Evaluating of the Stewart Platform. International Conference Graphicon.
MacNeilage et al., 2007
P.R. MacNeilage,M.S. Banks,D.R. Berger,H.H. Bulthoff
A Bayesian model of the disambiguation of gravitoinertial force by visual cues
Experimental Brain Research, 179 (2007), pp. 263-290 http://dx.doi.org/10.1007/s00221-006-0792-0
Merlet, 2006
J.P. Merlet
Parallel robots
Springer, (2006)
Nahon and Reid, 1990
M.A. Nahon,L.D. Reid
Simulator motion-drive algorithms - A designer's perspective
Journal of Guidance, Control, and Dynamics, 13 (1990), pp. 356-362
Reid and Nahon, 1985
L.D. Reid,M.A. Nahon
Flight Simulation Motion-Base Drive Algorithms: Part 1 - Developing and Testing the Equations
University of Toronto, UTIAS, (1985)pp. 296
Reid and Nahon, 1986a
L.D. Reid,M.A. Nahon
Flight Simulation Motion-Base Drive Algorithms: Part 2- Selecting the System Parameters
University of Toronto, UTIAS, (1986)pp. 307
Reid and Nahon, 1986b
L.D. Reid,M.A. Nahon
Flight Simulation Motion-Base Drive Algorithms: Part 3 - Pilot Evaluations
University of Toronto, UTIAS, (1986)
Reymond and Kemeny, 2000
G. Reymond,A. Kemeny
Motion Cueing in the Renault Driving Simulator
Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 34 (2000), pp. 249-259
Schmidt and Conrad, 1969
S.F. Schmidt,B. Conrad
The Calculation of Motion Drive Signals for Piloted Flight Simulators
NASA, (1969)
69-17
Sinacori, 1977
J.B. Sinacori
The Determination of Some Requirements for a Helicopter Flight Research Simulation Facility
Moffet Field, (1977)pp. 7805
Slob, 2008
J.J. Slob
State-of-the-Art Driving Simulators, a Literature Survey
Eindhoven University of Technology, (2008)
Stewart, 1965
Stewart, D., 1965. A Platform with six degrees of freedom.
Stahl et al., 2014
Stahl, K., Abdulsamad, G., Leimbach, K., Vershinin, Y. A., 2014. State of the Art and Simulation of Motion Cueing Algorithms for a Six Degree of Freedom Driving Simulator. Paper presented at the 17th International Conference on Intelligent Transportation Systems (ITSC).

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