Improving Helicopter Simulator Fidelity with Augmented Reality

Development and Testing of a Modified Flight Research Simulator where Virtual Windows are made visible through AR Goggles

Master Thesis (2020)
Author(s)

A. Pardi (TU Delft - Aerospace Engineering)

Contributor(s)

M Mulder – Mentor (TU Delft - Control & Simulation)

Olaf Stroosma – Graduation committee member (TU Delft - Control & Simulation)

MM van Paassen – Coach (TU Delft - Control & Simulation)

Faculty
Aerospace Engineering
Copyright
© 2020 Alessandro Pardi
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Alessandro Pardi
Graduation Date
08-10-2020
Awarding Institution
Delft University of Technology
Programme
['Aerospace Engineering']
Faculty
Aerospace Engineering
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Abstract

This project investigates the use of Augmented Reality (AR) in the context of research flight simulators to increase their visual fidelity in rotary-wing applications. The field of view of existing research simulators is often designed after fixed-wing aircraft and can be largely inferior to what is required to simulate helicopters. In particular, visibility downward and to the far side of the cockpit is limited, reducing the field of view of the outside environment available to the pilot. This can be potentially corrected by means of AR goggles adding virtual chin and side windows, supplementing those present in the simulator’s cabin. The visual system of the SIMONA Research Simulator (SRS) was complemented with AR goggles generating additional outside visuals, and this prototype was tested in a human-inthe-loop experiment where flight performance, workload and motion sickness parameters were evaluated in a precision piloting task, with and without the AR visuals. Results confirmed that the prototype was functionally working, with the selected piloting task having been completed both with and without the added AR visuals. However, further analysis found no significant effects of the AR visuals’ presence on flight performance, workload and motion sickness. It was concluded that the high levels of noise found in the data were caused by a mismatch between the piloting task and the participants population.

Files

TH_Final_Report.pdf
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