The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022

Parry, Jonathon; Costello, Donald H.; Rupert, Jason K.; Taylor, Gavin

doi:10.1002/sys.21703

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

DNN Based Ranging in Support of Autonomous Aerial Refueling

Costello,

DeVries,

Mauldin

et al. 2023

J Intell Robot Syst

View full text Add to dashboard Cite

DNN Based Ranging in Support of Autonomous Aerial Refueling

Costello,

DeVries,

Mauldin

et al. 2023

J Intell Robot Syst

View full text Add to dashboard Cite

Flying is for Droids: A Survey of Research into Generating Certification Evidence for AI/ML Algorithms to Replace Human Pilots

Costello,

2024

2024 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

Automatic Ground-Truth Image Labeling for Deep Neural Network Training and Evaluation Using Industrial Robotics and Motion Capture

Helmich,

Doherty,

Costello

et al. 2024

Journal of Verification, Validation and Uncertainty Quantification

View full text Add to dashboard Cite

The United States Navy intends to increase the amount of uncrewed aircraft in a carrier air wing. To support this increase, carrier based uncrewed aircraft will be required to have some level of autonomy as there will be situations where a human cannot be in/on the loop. However, there is no existing and approved method to certify autonomy within Naval Aviation. In support of generating certification evidence for autonomy, the United States Naval Academy has created a training and evaluation system to provide quantifiable metrics for feedback performance in autonomous systems. The preliminary use-case for this work focuses on autonomous aerial refueling. Prior demonstrations of autonomous aerial refueling have leveraged a deep neural network (DNN) for processing visual feedback to approximate the relative position of an aerial refueling drogue. The training and evaluation system proposed in this work simulates the relative motion between the aerial refueling drogue and feedback camera system using industrial robotics. Ground truth measurements of the pose between camera and drogue is measured using a commercial motion capture system. Preliminary results demonstrate calibration methods providing ground truth measurements with millimeter precision. Leveraging this calibration, the proposed system is capable of providing large-scale data sets for DNN training and evaluation against a precise ground truth.

show abstract

The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022

Cited by 4 publications

References 22 publications

DNN Based Ranging in Support of Autonomous Aerial Refueling

DNN Based Ranging in Support of Autonomous Aerial Refueling

Flying is for Droids: A Survey of Research into Generating Certification Evidence for AI/ML Algorithms to Replace Human Pilots

Automatic Ground-Truth Image Labeling for Deep Neural Network Training and Evaluation Using Industrial Robotics and Motion Capture

Contact Info

Product

Resources

About