Development and evaluation of an enhanced haptic-based virtual reality flight simulator for stratospheric airships
Bibliographic record
Abstract
Purpose The ground control station (GCS) is an important part of unmanned aerial vehicles (UAVs) which provides the facility for human control. In previous work, the authors developed an enhanced virtual reality GCS (VR-GCS) for airships. Here, the authors incorporated haptic gloves to control the aerial vehicle with the use of a virtual controller defined within the virtual environment. Design/methodology/approach The VR headset was connected to the haptics and the flight simulation tool. The VR headset was used to visualize basic flight simulation while the vehicle was controlled via the haptic gloves and a virtual controller defined in the virtual environment. Here, using the previous experience, the position and orientation data from the VR headset was sent to the FlightGear flight simulator (FGFS) via extensible markup language codes. This was used to drive the heads-up-display (HUD) as well within the VR headset. Then, the inputs from the pilot on the virtual controller were sent to the FGFS using an embedded code. To accurately simulate the final goal of deploying the haptic-based VR solution to monitor and pilot the airship in beyond visual line-of-sight scenarios, a VR application was developed using the Unity game engine. Finally, the integration of VR, haptics and FGFS was performed using another embedded code. Findings A test procedure was conducted with a similar rating technique based on the NASA TLX questionnaire that identifies the pilot’s spare mental capacity when completing an assigned task to assure the comfortability of the proposed haptics VR-HMD (HVR-HMD). Accordingly, 10 users participated in the test and a comparison has been made for the aircraft control using the physical remote control (RC) controller and the virtual one. The results from the repeated measures analysis of variance and Tukey’s honestly significant difference post hoc tests revealed significant differences in mental demand, physical demand, effort and frustration across the different simulation conditions. Notably, the HVR-HMD system significantly lowered workload and frustration levels compared to both the desktop and VR-HMD setups, underscoring its effectiveness as a training tool. Results from the NASA TLX questionnaire showed that the current iteration of the system is ideal for training amateur users to replace traditional RC controllers by using similar virtual systems in a safe and immersive environment. Originality/value Such an advanced portable system may increase the situational awareness of pilots and allow them to complete flights with the same data transmission procedures using virtual systems in simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".