Collaborative UAVs Intelligent Decision Optimization

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1569

Special Issue Editor


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Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: optimization and control of unmanned autonomous system; cooperative optimization and control; predictive control; nonlinear control

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have become increasingly autonomous, intelligent, and collaborative. Multiple UAVs are able to work together to complete sophisticated missions beyond the capabilities of a single UAV. However, the task of optimizing the decision making and cooperative control of collaborative UAV teams presents significant challenges due to constraints such as limited communication bandwidth, computational power, and physical sensor capabilities. 

Cooperative control of multi-UAV systems is an active area of research. UAV swarms and fleets have demonstrated complex coordinated behaviors and mobility. However, challenges remain in tasks such as precision formation control, real-time motion planning with collision avoidance, and dynamic reconfiguration. Key issues also include the optimal allocation of limited resources such as sensing, communication, and computation across the UAV network. 

This Special Issue seeks original contributions that propose and evaluate new techniques, methods, and frameworks to enable intelligent decision optimization and cooperative control of collaborative UAV systems. Topics of interest include but are not limited to: 

  • Distributed optimization algorithms for multi-agent collaborative decision making and cooperative control;
  • Reinforcement learning-based collaborative decision optimization and cooperative control methods;
  • Hybrid model-based optimization and learning frameworks for decision making and cooperative control;
  • Novel system architectures for collaborative UAV decision making and cooperative control (e.g., leader-follower, behavior composition);
  • Motion planning and formation control for cooperative UAV systems;
  • Applications of collaborative UAV decision optimization and cooperative control (e.g., search and rescue, collaborative mapping). 

High quality papers based on theories, applications, or experimental data are welcome.

Dr. Chaofang Hu
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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14 pages, 4184 KiB  
Article
Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets
by Ahram Song
Aerospace 2023, 10(10), 880; https://doi.org/10.3390/aerospace10100880 - 12 Oct 2023
Cited by 1 | Viewed by 1328
Abstract
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), [...] Read more.
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily due to environmental factors such as atmospheric conditions and relief displacement. Unmanned aerial vehicle (UAV) imagery presents unique challenges, such as variations in object appearance due to UAV flight altitude and shadows in urban areas. This study analyzed the combined segmentation network (CSN) designed to train heterogeneous UAV datasets effectively for their segmentation performance across different data types. Results confirmed that CSN yielded high segmentation accuracy on specific classes and can be used on diverse data sources for UAV image segmentation. The main contributions of this study include analyzing the impact of CSN on segmentation accuracy, experimenting with structures with shared encoding layers to enhance segmentation accuracy, and investigating the influence of data types on segmentation accuracy. Full article
(This article belongs to the Special Issue Collaborative UAVs Intelligent Decision Optimization)
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