ABSTRACT
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem.
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- Reymond, G. and A. Kemeny, Motion Cueing in the Renault Driving Simulator. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 2000. 34: p. 249--259.Google Scholar
- Sinacori, J.B., The Determination of Some Requirements for a Helicopter Flight Research Simulation Facility. 1977, Moffet Field: CA, USA.Google Scholar
- Reid, L. D. and M. A. Nahon, Flight Simulation Motion-Base Drive Algorithms: Part 1 - Developing and Testing the Equations. 1985, UTIAS: University of Toronto.Google Scholar
- Reid, L. D. and M. A. Nahon, Flight Simulation Motion-Base Drive Algorithms: Part 2 - Selecting the System Parameters. 1986, UTIAS: University of Toronto.Google Scholar
- Casas, S., et al., Motion-Cuing Algorithms: Characterization of Users' Perception. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2015. 57(1): p. 144--162.Google Scholar
- Casas, S., et al., Towards a simulation-based tuning of motion cueing algorithms. Simulation Modelling Practice and Theory, 2016. 67: p. 137--154.Google Scholar
- Kennedy, J. and R. Eberhart. Particle Swarm Optimization. in IEEE International Conference. on Neural Networks. 1995. Perth, WA, Australia: IEEE Service Center, Piscataway, NJ, USA.Google Scholar
- Shih-Wei, L., et al., Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 2008. 35: p. 1817--1824. Google ScholarDigital Library
- Asadi, H, et al. A Particle Swarm Optimization-based washout filter for improving simulator motion fidelity, in Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on. 2016. IEEE.Google Scholar
- Casas, S., et al., Towards an extensible simulator of real motion platforms. Simulation Modelling Practice and Theory, 2014. 45(0): p. 50--61.Google Scholar
Index Terms
- Applying particle swarm optimization to the motion-cueing-algorithm tuning problem
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