Flight Test

Flight testing encompasses the experimental evaluation of aircraft and unmanned aerial vehicle (UAV) performance, encompassing diverse objectives like autonomous navigation, energy-efficient perching, and payload manipulation. Current research emphasizes the development and validation of advanced control algorithms, including reinforcement learning, model predictive control, and nonlinear dynamic inversion, often coupled with data-driven approaches like Gaussian processes and neural networks for improved robustness and efficiency. These advancements are crucial for enhancing safety, autonomy, and operational capabilities in various applications, from urban air mobility to space exploration, by providing rigorous validation of theoretical models and enabling the development of more reliable and sophisticated systems.

Papers

March 14, 2023

GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics
Francesco Crocetti, Jeffrey Mao, Alessandro Saviolo, Gabriele Costante, Giuseppe Loianno
Application Proficiency Gaussian Process Aerial Robot Flight Test Quadrotor Dynamic Online Regression GP Inference

March 7, 2023

Virtual Omnidirectional Perception for Downwash Prediction within a Team of Nano Multirotors Flying in Close Proximity
Akmaral Moldagalieva, Wolfgang Hönig
Team Dynamic Flight Test Close Proximity Nano Quadrotor Omnidirectional Audio Visual

February 3, 2023

Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model
J. J. Tong, W. Zhang, F. Liao, C. F. Li, Y. F. Zhang
Machine Learning Flight Data Flight Test Propeller Based Data Generation Model Unmanned Arial Vehicle

November 12, 2022

Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV
Nishanth Rao, Suresh Sundaram, Varun Raghavendra
Kalman Filter Linear Estimator Memory Network Economic Operation Micro Aerial Vehicle Flight Test Position Estimation

October 24, 2022

Experimental Flight Testing of a Fault-Tolerant Adaptive Autopilot for Fixed-Wing Aircraft
Joonghyun Lee, John Spencer, Siyuan Shao, Juan Augusto Paredes, Dennis S. Bernstein, Ankit Goel
Flight Test Aircraft Design Time Varying Control Adaptive Autopilot

September 9, 2022

Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather
Haotian Gu, Hamidreza Jafarnejadsani
Safety Critical Control Autonomous Landing Flight Test Vehicle Level Level Adaptation

January 10, 2022

Neither Fast Nor Slow: How to Fly Through Narrow Tunnels
Luqi Wang, Hao Xu, Yichen Zhang, Shaojie Shen
Safe Exploration Flight Test Smooth Path

December 7, 2021

In-flight Novelty Detection with Convolutional Neural Networks
Adam Hartwell, Felipe Montana, Will Jacobs, Visakan Kadirkamanathan, Andrew R Mills, Tom Clark
Convolutional Neural Network Synthetic Data Data Driven Novelty Detection Flight Test Turbofan Engine

November 17, 2021

Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach
Erik Vargo, Alex Tien, Arian Jafari
Convolutional Neural Network Demand Prediction Flight Test Alert System Airport Runway Segmentation

Flight Test

Papers

GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics

Virtual Omnidirectional Perception for Downwash Prediction within a Team of Nano Multirotors Flying in Close Proximity

Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model

Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV

Experimental Flight Testing of a Fault-Tolerant Adaptive Autopilot for Fixed-Wing Aircraft

Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather

Neither Fast Nor Slow: How to Fly Through Narrow Tunnels

In-flight Novelty Detection with Convolutional Neural Networks

Airport Taxi Time Prediction and Alerting: A Convolutional Neural Network Approach