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Motion Cueing Algorithms: A Review: Algorithms, Evaluation and Tuning

Motion Cueing Algorithms: A Review: Algorithms, Evaluation and Tuning
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Author(s): Sergio Casas (University of Valencia, Valencia, Spain), Ricardo Olanda (University of Valencia, Valencia, Spain) and Nilanjan Dey (Techno India College of Technology, West Bengal, India)
Copyright: 2017
Volume: 1
Issue: 1
Pages: 17
Source title: International Journal of Virtual and Augmented Reality (IJVAR)
Editor(s)-in-Chief: Cristina Portales Ricart (Universitat de Valencia, Spain) and Marcos Fernández Marin (Universidad de Valencia, Spain)
DOI: 10.4018/IJVAR.2017010107

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Abstract

Robotic motion platforms are commonly used in motion-based vehicle simulation. However, the reproduction of realistic accelerations within a reduced workspace is a major challenge. Thus, high-level control strategies commonly referred to as motion cueing algorithms (MCA) are required to convert the simulated vehicle physical state into actual motion for the motion platform. This paper reviews the most important strategies for the generation of motion cues in simulators, listing the advantages and drawbacks of the different solutions. The motion cueing problem, a general scheme and the four most common approaches – classical washout, adaptive washout, optimal control and model predictive control – are presented. The existing surveys of the state-of-the-art on motion cueing are usually limited to list the MCA or to a particular vehicle application. In this work, a comprehensive vehicle-agnostic review is presented. Moreover, evaluation and tuning of MCA are also considered, classifying the different methods, and providing examples of each class.

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