118018Hanoi University of Science, Vietnam.
Sci Prog. 2022 Apr-Jun;105(2):368504221104333. doi: 10.1177/00368504221104333.
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 ClCy algorithm, the classical algorithm developed for the standart six-degree-of-freedom simulators was a sample implementation due to its popular and familiar characteristics. Secondly, the proposal of the use of the mean-variance mapping optimization (MVMO) for auto-tuning parameters of the two algorithms for reducing both rotational false cues in roll and pitch channel, and longitudinal acceleration as well as washout effect. The simulation results prove that 1) The classical and other MCAs can be applied in the novel motion platform with the proposed motion conversion; 2) both algorithms with auto-tuned parameters have high performance in exploiting effectively the workspace of the motion platform, producing no false cues of angular velocity, conpensating the disturbed longitudinal acceleration, and pulling the motion platform to the initial position after the simulation task; 3) The auto-tuning method is so transparent that can manipulates the specific simulated quantities according to the tuning goals.
驾驶模拟器已经被广泛用于测试和评估产品和服务,其复杂性和价格范围从极其简单的低成本模拟器(带有固定底座)到非常复杂的高端、昂贵的六自由度模拟器(带有 XY 工作台)。最近的一项新技术利用工业机器人——库卡(KUKA)摇摆模拟器作为交互式运动模拟器平台,不仅具有高度灵活的工作空间,而且由于基本机械结构的大规模生产,价格也显著降低。在驾驶模拟器的约束工作空间中,运动提示算法(MCA)通常用于合并倾斜重力和平移加速度分量,以模拟真实车辆中的线性加速度。然而,只有少数 MCA 是为运动平台开发的,几乎所有的 MCA 都是在笛卡尔坐标系下为标准六自由度模拟器实现的。在圆柱坐标系中(ClCy)MCA 是为新型运动平台开发的第一个 MCA,以利用巨大的旋转运动来模拟横向加速度,同时通过适当的俯仰倾斜角度补偿旋转运动中出现的令人烦恼的纵向加速度(由于离心力出现在旋转运动中)。由于试验和错误方法以及旋转运动产生的干扰运动提示,为新型运动平台调整 MCA 的过程非常耗时,因此需要专家的参与。尽管有几种基于模糊控制理论或遗传优化方法的经典、最优和模型预测控制 MCA 的自动调谐方法,但这些方法纯粹适用于笛卡尔坐标系,没有考虑到令人烦恼的纵向加速度。因此,本文首先介绍了利用旋转运动模拟横向加速度的新型运动平台中 MCA 的集成过程。对于这种情况,除了 ClCy 算法之外,还为标准六自由度模拟器开发了经典算法作为示例实现,因为它具有流行和熟悉的特点。其次,提出了使用均值方差映射优化(MVMO)自动调整两种算法的参数的方法,以减少滚转和俯仰通道中的旋转虚假提示以及纵向加速度和冲洗效应。仿真结果表明:1)经典和其他 MCA 可以应用于新型运动平台,并提出了运动转换;2)具有自动调谐参数的两种算法在有效利用运动平台工作空间方面具有高性能,不会产生角速度的虚假提示,补偿了受干扰的纵向加速度,并在模拟任务结束后将运动平台拉回到初始位置;3)自动调谐方法非常透明,可以根据调谐目标操纵特定的模拟量。