Abstract
Motion simulators are becoming increasingly popular for many applications in which human sensation is important to replicate and optimize target motions. For the emulation of the perceived human acceleration, motion cueing algorithms (MCAs) have been proposed in the literature that mimics the motion sensation by a combination of actual acceleration and tilted gravity effects, termed g-force or specific force. However, their relative performance has not yet been analyzed. This paper reviews existing families of MCAs and compares their performance for a simple offline S-shaped planar test trajectory featuring only lateral acceleration. The comparison is carried out both numerically using two previously published objective measures, the "performance indicator" of Pouliot, Gosselin, and Nahon, and the "good criterion" of Schmidt, as well as subjectively by a preliminary passenger rating on a real motion platform-Robocoaster testbed. The results show that (a) the novel optimizing MCA group exploits more effectively the workspace of the motion platform than the traditional MCA group for reducing false cue with small scale error and shape errors, (b) path-dependent tuning of MCA parameters may improve motion sensation, (c) average subjective ratings can be made to correlate well with the "good criterion" when expanded with a penalty for false angular velocity cues, and (d) the scale error of specific force seems to play the most important role to the evaluation of test subject on the motion cue quality. However, still a strong variance in subjective ratings was observed, making further research necessary.