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Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial...
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Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 6089

Special Issue Editor

Dr. Xiaosong Du

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 400 W 13th Street, Rolla, MO 65409, USA
Interests: machine learning; deep learning; artificial lntelligence; aircraft design optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban air mobility (UAM) opens up the possibility of a new era for air travel. UAM has emerged as a safe and efficient air transportation system where everything, from small package delivery drones to passenger aircraft, is operating above populated areas. However, along with these promising benefits, challenges also arise. In particular, we need to create complex coupled system-level designs, realize fast interactive decision making, and maintain a higher system reliability and robustness by incentivizing cutting-edge research. One of the main forces is artificial intelligence (AI), which especially enables various design optimization architectures (such as optimal inverse design mapping and multi-fidelity design), realizes real-time decision making via surrogate modeling, and considers uncertainty within design optimization. In addition, researchers also utilize AI for computer vision, intelligent parameterization, optimal trajectory design, optimal experimental design, etc.

In line with the goal of the Mathematics journal, the aim of this Special Issue is to provide a platform where researchers present their original research contributions. In particular, this Special Issue focuses on AI and design optimization in UAM and expects to inspire novel research products with large-scale practical applications.

Dr. Xiaosong Du
Guest Editor

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Keywords

  • urban air mobility
  • drones
  • aircraft
  • artificial intelligence
  • design optimization
  • surrogate modeling
  • optimal trajectory design
  • optimal experimental design

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Published Papers (4 papers)

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Research

21 pages, 3543 KiB  
Article
Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
by Hao Chen, Fan Luo, Jianguo Zhou and Yanming Dong
Mathematics 2024, 12(17), 2672; https://doi.org/10.3390/math12172672 - 28 Aug 2024
Viewed by 543
Abstract
Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This [...] Read more.
Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This study leverages the Deep Double Q-Learning Network (DDQN) algorithm to optimize the traditional Greedy Perimeter Stateless Routing (GPSR) protocol, resulting in a multi-objective optimized GPSR routing protocol (DDQN-MTGPSR). By constructing a multi-objective routing optimization model through cross-layer data fusion, the proposed approach aims to enhance UAV network communication performance comprehensively. In addition, this study develops the above DDQN-MTGPSR intelligent routing algorithm based on the NS-3 platform and uses an artificial intelligence framework. In order to verify the effectiveness of the DDQN-MTGPSR algorithm, it is simulated and compared with the traditional ad hoc routing protocols, and the experimental results show that compared with the GPSR protocol, the DDQN-MTGPSR has achieved significant optimization in the key metrics such as the average end-to-end delay, packet delivery rate, node average residual energy variance and percentage of node average residual energy. In high dynamic scenarios, the above indicators were optimized by 20.05%, 12.72%, 0.47%, and 50.15%, respectively, while optimizing 36.31%, 26.26%, 8.709%, and 69.3% in large-scale scenarios, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles)
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25 pages, 1714 KiB  
Article
Optimal Tilt-Wing eVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks
by Shuan-Tai Yeh and Xiaosong Du
Mathematics 2024, 12(1), 26; https://doi.org/10.3390/math12010026 - 21 Dec 2023
Cited by 3 | Viewed by 1409
Abstract
Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization [...] Read more.
Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus A3 Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles)
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18 pages, 7011 KiB  
Article
A Cooperative Game Hybrid Optimization Algorithm Applied to UAV Inspection Path Planning in Urban Pipe Corridors
by Chuanyue Wang, Lei Zhang, Yifan Gao, Xiaoyuan Zheng and Qianling Wang
Mathematics 2023, 11(16), 3620; https://doi.org/10.3390/math11163620 - 21 Aug 2023
Cited by 2 | Viewed by 1366
Abstract
This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D [...] Read more.
This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles)
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23 pages, 4681 KiB  
Article
Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior
by Faten Aljalaud, Heba Kurdi and Kamal Youcef-Toumi
Mathematics 2023, 11(9), 2092; https://doi.org/10.3390/math11092092 - 28 Apr 2023
Cited by 6 | Viewed by 1790
Abstract
This paper presents a novel path planning heuristic for multi-UAV pipe inspection missions inspired by the booby bird’s foraging behavior. The heuristic enables each UAV to find an optimal path that minimizes the detection time of defects in pipe networks while avoiding collisions [...] Read more.
This paper presents a novel path planning heuristic for multi-UAV pipe inspection missions inspired by the booby bird’s foraging behavior. The heuristic enables each UAV to find an optimal path that minimizes the detection time of defects in pipe networks while avoiding collisions with obstacles and other UAVs. The proposed method is compared with four existing path planning algorithms adapted for multi-UAV scenarios: ant colony optimization (ACO), particle swarm optimization (PSO), opportunistic coordination, and random schemes. The results show that the booby heuristic outperforms the other algorithms in terms of mean detection time and computational efficiency under different settings of defect complexity and number of UAVs. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles)
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