Helicopter operations require a well-controlled and minimal lateral drift shortly before ground contact. Any lateral speed exceeding this small threshold can cause a dangerous momentum around the roll axis, which may cause a total roll over of the helicopter. As long as pilots can observe visual cues from the ground, they are able to easily control the helicopter drift. But whenever natural vision is reduced or even obscured, e.g. due to night, fog, or dust, this controllability diminishes. Therefore helicopter operators could benefit from some type of “drift indication” that mitigates the influence of a degraded visual environment. Generally humans derive ego motion by the perceived environmental object flow. The visual cues perceived are located close to the helicopter, therefore even small movements can be recognized. This fact was used to investigate a modified drift indication. To enhance the perception of ego motion in a conformal HMD symbol set the measured movement was used to generate a pattern motion in the forward field of view close or on the landing pad. The paper will discuss the method of amplified ego motion drift indication. Aspects concerning impact factors like visualization type, location, gain and more will be addressed. Further conclusions from previous studies, a high fidelity experiment and a part task experiment, will be provided. A part task study will be presented that compared different amplified drift indications against a predictor. 24 participants, 15 holding a fixed wing license and 4 helicopter pilots, had to perform a dual task on a virtual reality headset. A simplified control model was used to steer a “helicopter” down to a landing pad while acknowledging randomly placed characters.

Date Published: 13 May 2016
PDF: 10 pages
Proc. SPIE 9839, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2016, 983908 (13 May 2016); doi: 10.1117/12.2224706

Published in SPIE Proceedings Vol. 9839:
Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2016
Jack Sanders-Reed; Jarvis J. Arthur III, Editor(s)