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Dear Colleagues,

Unmanned Aerial Systems play an increasingly remarkable role in widely diffused application fields, from military defense programs and strategies to civil and commercial utilizations. UAS are usually involved in Dull, Dirty and Dangerous (DDD) scenarios, which require reliable, extended capability, easy-to-use and cost-effective fixed-wing or rotary-wing platforms. Therefore, it is important to provide onboard systems capable of recognizing the environment around the aerial vehicle, detecting and avoiding obstacles, implementing path planning and management strategies, defining safe landing areas, and achieving full autonomy, especially for BVLOS (Beyond Visual Line-Of-Sight) missions. The technical and economic challenges implied by the issues related to autonomous navigation range from hardware (sensors, platforms, controllers, etc.) to software (data processing and filtering techniques, optimal control, state estimation, innovative algorithms, etc. ), and from modeling to practical realizations.

The aim of this Special Issue is to seek high-quality contributions that highlight novel research results and emerging applications addressing recent breakthroughs in UAS autonomous navigation and related fields, such as flight mechanics and control, structure design, sensors design, etc.

The topics of interest include the following:

2D and 3D mapping, target detection, obstacle avoidance;
Active perception of targets in cluttered environments (foliage, forest, etc.);
Vision-based and optical flow techniques;
Sensors and sensor fusion techniques;
Design models for guidance and controlled flight;
State estimation, data analysis and filtering techniques (KF, EKF, particle filtering, fuzzy logic, etc.);
Path planning and path management;
Optimal control and strategies (neural networks, fuzzy logic, reinforcement learning, evolutionary and genetic algorithms, AI, etc.);
Navigation in GPS-denied environments;
Autolanding, safe landing area definition (SLAD);
Environmental effects on UAVs (wind, etc.);
Autonomous UAV or MAV swarms, distributed architectures;
BVLOS autonomous navigation.

Dr. Umberto Papa
Dr. Giuseppe Del Core
Dr. Salvatore Ponte
Dr. Gennaro Ariante
Guest Editors

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Keywords

unmanned aircraft systems
autonomous navigation
flight mechanics and dynamics
sensor fusion
filtering techniques
optimization algorithms
state estimation

Published Papers (1 paper)

Research

27 pages, 1824 KiB

Open AccessArticle

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

by Shuan-Tai Yeh and Xiaosong Du

Electronics 2024, 13(10), 1911; https://doi.org/10.3390/electronics13101911 - 13 May 2024

Viewed by 541

Abstract

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus

A^{3}

Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential. Full article

(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, Volume II)

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