Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 482

Special Issue Editor


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Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 400 W 13th Street, Rolla, MO 65409, USA
Interests: machine learning and artificial intelligence; aircraft design optimization; unmanned aerial vehicles
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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 every vehicle involved, 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 higher system reliability and robustness by incentivizing cutting-edge research. One of the main forces is artificial intelligence (AI), which 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 for researchers to present their original research contributions. In particular, this Special Issue focuses on AI and design optimization in UAM and hopes 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 (1 paper)

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Research

39 pages, 3695 KiB  
Article
Fast Identification and Detection Algorithm for Maneuverable Unmanned Aircraft Based on Multimodal Data Fusion
by Tian Luan, Shixiong Zhou, Yicheng Zhang and Weijun Pan
Mathematics 2025, 13(11), 1825; https://doi.org/10.3390/math13111825 - 30 May 2025
Viewed by 323
Abstract
To address the critical challenges of insufficient monitoring capabilities and vulnerable defense systems against drones in regional airports, this study proposes a multi-source data fusion framework for rapid UAV detection. Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four [...] Read more.
To address the critical challenges of insufficient monitoring capabilities and vulnerable defense systems against drones in regional airports, this study proposes a multi-source data fusion framework for rapid UAV detection. Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four key innovations: (1) A dual-path RGB-IR fusion architecture that exploits complementary multi-modal data; (2) C3k2-DATB dynamic attention modules for enhanced feature extraction and semantic perception; (3) A bilevel routing attention mechanism with agent queries (BRSA) for precise target localization; (4) A semantic-detail injection (SDI) module coupled with windmill-shaped convolutional detection heads (PCHead) and Wasserstein Distance loss to expand receptive fields and accelerate convergence. Experimental results demonstrate superior performance with 99.3% mAP@50 (17.4% improvement over baseline YOLOv11), while maintaining lightweight characteristics (2.54M parameters, 7.8 GFLOPS). For practical deployment, we further enhance tracking robustness through an improved BoT-SORT algorithm within an interactive multiple model framework, achieving 91.3% MOTA and 93.0% IDF1 under low-light conditions. This integrated solution provides cost-effective, high-precision drone surveillance for resource-constrained airports. Full article
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