Abstract
Motion cueing algorithms (MCAs) are the cheating technique to drive physical simulators along offline-generated trajectories such as to produce proper motion cues to the passenger by a combination of tilt gravity and translational acceleration components (so-called linear specific forces or “g-forces”). In this paper, the numerical perception quality of the aforementioned families of MCAs is compared for a virtual test ride along a simple S-shaped planar curve featuring solely lateral accelerations, using two previously published objective measures. To this end, the MCAs were implemented using originally published MCA parameter values and optimally tuned values concerning the test trajectory. The comparison shows large discrepancies between the different MCAs, both in terms of simulated motion perception and fulfillment of robot workspace constraints. The best-fit tuning of MCA parameters for a given trajectory may significantly improve the objective measures. In this study, a new auto-tuning method based on a generic optimal method called mean variance mapping optimization to minimize the performance indicator was introduced to find the off-line optimal algorithm’s tuned parameters. Three testbeds using the auto-tuning process show different tuned parameter sets, however, the off-line optimal MCA has a similar response with all set of tuned parameters.
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This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2020-SAHEP-013.
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Pham, DA. (2021). Numerical Comparison of Offline Motion-Cueing Algorithms for the Ride Simulator RoboCoaster and Auto-tuning Parameters. In: Long, B.T., Kim, YH., Ishizaki, K., Toan, N.D., Parinov, I.A., Vu, N.P. (eds) Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). MMMS 2020. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-69610-8_127
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DOI: https://doi.org/10.1007/978-3-030-69610-8_127
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