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A sliding mode-based approach to motion cueing for virtual reality gaming using motion simulators

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Abstract

Motion simulators have been of significant importance for the aviation sector in training pilots. However, the present boom in the utilization of robotics for virtual reality (VR) gaming has given rise to a new application of motion simulators. Motion cueing algorithms (MCA) play a key role in mapping the motions from a gaming scenario to the workspace of a simulator. This workspace is small (as compared to the gaming world), and on reaching the boundary, it becomes necessary to saturate the motion. Each degree of freedom, in the Cartesian space, is saturated between two fixed extremities. This hampers the perception of motion of a user enjoying the scenario. In order to address this practical problem, we make an attempt to enlarge the workspace and develop a mathematical methodology to prevent the simulator from exiting a non-cuboidal workspace. To do so, we propose sliding mode-based cueing algorithm (SMCA), which makes the simulator to slide in close proximity across the boundary of workspace. We make use of discrete-time models to present this methodology in order to ensure straightforward implementation by researchers in the future. Veracity of SMCA is testified by means of experimentation on SP7 motion simulator. The experimental results give evidence of a 57% increase in the considered sub-workspace, thereby reducing the relative necessity to saturate the motions as compared to classical MCA. This leads to a better experience of a user enjoying the VR scenario. On the other hand, the following drawbacks are reported: (1) necessity to analytically model the workspace boundary and ensuring that it is smooth with nonzero gradient, (2) SMCA parameter selection is more cumbersome than classical MCA, thereby making its utility restricted to recorded scenarios.

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Sharma, A., Ikbal, M.S., Cuong, D.T. et al. A sliding mode-based approach to motion cueing for virtual reality gaming using motion simulators. Virtual Reality 25, 95–106 (2021). https://doi.org/10.1007/s10055-020-00439-5

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