Motion simulation is widely used for various applications, such as training, rehabilitation, product development and research. Recent technological advances have improved the realism and therefore the usefulness of motion simulation considerably. These advances, however, mainly concern technology such as vehicle models, visual rendering, control loading and quality of the auditory stimuli. One aspect of motion simulation that has not benefited equally from technological advances, and still remains a main challenge when using a motion simulator, is the motion cueing.
Motion cueing is the process of converting a desired physical motion into commands that are sent to the motion simulator. This conversion is done by an algorithm, called a motion cueing algorithm (MCA). Over the decades, many different types of MCAs have been introduced. The vast majority of them are derivatives of the original approach, which is now widely known as the "classical approach". This classical approach relies mainly on scaling down and filtering the physical motion such that the result "fits" within the limited motion space of a motion simulator.
Within the WABS-project ("Wahrnehmungsbasierte Bewegungssimulation") we developed an alternative approach to motion cueing. This approach is based on the idea that motion cueing can be improved by including novel insights in human self-motion perception, obtained from fundamental motion perception studies, in the MCAs. This "perception-based motion cueing" (PBMC) approach allows for exploiting the limitations and ambiguities of the human perceptual system. PBMC differs from the traditional approach of motion cueing in several ways. The most important difference is that PBMC aims to reproduce the perception of motion, instead of reproducing physical motion. Another important difference is that PBMC operates through optimizing simulator input commands, instead of filtering the motion that is to be reproduced.
Within the WABS project we developed and experimentally tested several PBMC algorithms. Next to the implementation of an advanced perception model, the work included the development of cost functions and the development of experimental methods that allow for reliably measuring the quality of an MCA. With those methods, we were able to experimentally show that PBMC algorithms are superior to the classical approach.