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Model predictive motion cueing for a helicopter hover task on an 8-DOF serial robot simulator

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Drop,  FM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Olivari,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133474

Geluardi,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Katliar,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Drop, F., Olivari, M., Geluardi, S., Katliar, M., & Bülthoff, H. (2019). Model predictive motion cueing for a helicopter hover task on an 8-DOF serial robot simulator. In 44th European Rotorcraft Forum 2018 (ERF) (pp. 1103-1116). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/21.11116/0000-0002-4782-B
Abstract
Motion cueing for helicopter hover is difficult: small simulators require considerable attenuation, rendering motion cues not useful for stabilization, and large simulators are typically not cost effective. Industrial serial robot-based simulators provide large motion capabilities at a moderate cost, but have two distinct disadvantages. First, they are highly dimensional systems with a non-convex motion space, such that efficient use of the entire space is not trivial. Second, they are typically non-stiff structures with a large mass at the end-effector, resulting in oscillatory dynamical properties. We recently developed a novel Model Predictive Motion Cueing Algorithm (MPMCA) that resolves both problems effectively for prerecorded inertial reference signals. The MPMCA requires an accurate prediction of the future course of the reference inertial signals, which is trivial for prerecorded maneuvers, but not for real-time human-in-the-loop simulations. In this paper, we present a model-based prediction method, which predicts pilot control inputs and the subsequent helicopter response during a helicopter hover simulation in real-time. The method is tested in a human-in-the-loop experiment and compared with the Classic Washout Algorithm. The results demonstrate that the MPMCA is a promising new approach to motion cueing.