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auto-tuning parameters of motion cueing algorithms for high performance driving simulator based on Kuka Robocoaster

SCIENCE PROGRESS 2022年第2期105卷 368504221104333-368504221104333页

作者： Duc-An Pham Duc-Toan Nguyen Hanoi Univ Sci Hanoi Vietnam

Driving simulators have been utilized to test and evaluate products and services for a long time. Their complexity and price range from extremely simple low-cost simulators with a fixed base to very complex high-end and pricey six-degree-of-freedom simulators with the XY table. The recent novel technique that uses an industrial robot - KUKA Robocoaster - as an interactive motion simulator platform, allowing for a highly flexible workspace as well as significantly lower prices due to mass production of the fundamental mechanics. In the constrained workspace of driving simulators, motion cueing algorithms (MCAs) are commonly employed to merge the tilt gravity and translational acceleration components for simulating the linear acceleration in the real vehicle. However, there is a few MCAs developed for the motion platform, almost MCAs were implemented for the standard six-degree-of-freedom simulators in the Cartesian coordinate. The classical MCA in the cylindrical coordinate (ClCy) MCA was first developed for the novel motion platform to take advantage of enormous rotational motion to simulate lateral acceleration while compensating for the bothersome longitudinal acceleration (due to centrifugal acceleration appearing in the rotational motion) with a proper pitch tilted angle. The process of tuning MCAs for the novel motion platform is time-consuming due to both trial and error method and the disturbing motion cues generated by rotational motion, thus it needs the involvement of experts. Although there are several auto-tuning approaches for classical, optimal, and model-predictive control MCA based on fuzzy control theory or genetic optimization method, the methods were purely applied for Cartersian coordinate without taking the bothersome longitudinal acceleration into account. Therefore, this paper firstly presents the process of integrating MCAs in the novel motion platform utilizing rotational motion for simulating lateral acceleration. For the case, besides the

关键词： Motion cueing algorithm KUKA Robocoaster motion platform auto-tuning parameters MVMO genetic optimization method

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Numerical comparison of offline motion-cueing algorithms for the ride simulator robocoaster and auto-tuning parameters 2nd

Numerical comparison of offline motion-cueing algorithms for...

引用

2nd Annual International Conference on Material, Machines and Methods for Sustainable Development, MMMS 2020

作者： Pham, Duc-An Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783030696092

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： auto-tuning parameters Motion cueing algorithm Objective critera

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