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To move or not to move? Analyzing motion cueing in vehicle simulators by means of massive simulations
Virtual Reality ( IF 4.2 ) Pub Date : 2019-06-18 , DOI: 10.1007/s10055-019-00387-9
Sergio Casas , Cristina Portalés , Pedro Morillo , Marcos Fernández

Motion platforms and motion cueing algorithms (MCA) have been included in virtual reality applications for several decades. They are necessary to provide suitable inertial cues in vehicle simulators. However, the great number of operational constraints that these devices and algorithms suffer, namely limited physical space, elevated costs, absence of sufficient power, difficulty of tuning and lack of standardized assessment methods, have hindered their widespread use. This work tries to clarify open questions in the field, such as: How important is MCA tuning? How much does size, number of DOF and power/latency matter? Can the absence of motion be better than poor motion cueing? What are the key factors that should be addressed to enhance the design of these devices? Although absolute certain answers cannot be given, this paper tries to clarify these research questions by performing massive experiments with simulated motion platforms of different types, sizes and powers. The information obtained from these experiments will be important to customize the design of real devices for this particular use. Ideally, subjective experiments with human experts would have been preferred. However, the use of simulated devices allows comparing many different motion platforms. In this paper, forty of these devices are simulated, optimized by means of a heuristic algorithm and compared with objective indicators in order to measure their relative performance using the classical MCA, something that would require an unreasonable amount of effort with real users and real devices. The obtained results show that MCA tuning is of the utmost importance in motion cueing. They also suggest that high power can usually compensate for lack of size and that a 6-DOF motion platform slightly improves the performance of a 3-DOF motion platform.

中文翻译:

要动还是不动?通过大规模仿真分析车辆模拟器中的运动提示

运动平台和运动提示算法(MCA)已被包含在虚拟现实应用程序中数十年。它们是在车辆模拟器中提供合适的惯性提示所必需的。然而,这些设备和算法所遭受的大量操作约束,即有限的物理空间,高昂的成本,缺乏足够的功率,调整困难以及缺乏标准化的评估方法,阻碍了它们的广泛使用。这项工作试图澄清该领域中的未解决问题,例如:MCA调整有多重要?大小,DOF数量和功率/等待时间有多重要?运动少可以比运动不佳提示好吗?增强这些设备设计应解决的关键因素是什么?尽管不能给出绝对的肯定答案,本文试图通过使用不同类型,大小和功率的模拟运动平台进行大规模实验来阐明这些研究问题。从这些实验中获得的信息对于为此特定用途定制真实设备的设计将非常重要。理想情况下,将首选与人类专家进行主观实验。但是,使用模拟设备可以比较许多不同的运动平台。在本文中,使用启发式算法对这些设备中的40个进行了仿真,优化,并与客观指标进行了比较,以便使用经典MCA来衡量它们的相对性能,这在实际用户和实际设备上需要付出不合理的工作量。获得的结果表明,MCA调整在运动提示中至关重要。
更新日期:2019-06-18
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