International Conference on Autonomous Unmanned Systems (5th ICAUS 2025)

Topic Information

Dear Colleagues,

This topic offers a unique and attractive platform for scientists, engineers, and practitioners worldwide to present and share their recent research and innovative ideas in unmanned systems, robotics, automation, and intelligent systems. The aim of the topic is to stimulate researchers active in areas pertinent to intelligent unmanned systems. The topics of interest include, but are not limited to, the following:

1. Unmanned aircraft systems;
2. Unmanned ground systems;
3. Unmanned surface/underwater systems;
4. Space unmanned systems;
5. Autonomous and cooperative control technology for unmanned systems;
6. Intelligent environment-sensing technology for unmanned systems;
7. Navigation and positioning technology for unmanned systems;
8. Communications and networking technology for unmanned systems;
9. Architecture, energy, and power technologies for unmanned systems;
10. Payload technologies for unmanned systems;
11. Task and effectiveness evaluation techniques for unmanned systems;
12. Modeling and simulation technologies for unmanned systems;
13. Intelligent information fusion and information processing in unmanned systems;
14. Multi-agent collaborative theory and associated technologies;
15. Brain–computer fusion and hybrid intelligence technology;
16. Artificial intelligence algorithms and their application in unmanned systems;
17. Bionic technology and its application in unmanned systems;
18. Unmanned systems’ countermeasures technologies;
19. Sociological theory and development of unmanned systems;
20. Unmanned systems’ educational platform, mode and practice;
21. New concepts in unmanned systems;
22. Other relevant theories, methods, and techniques for unmanned systems.

Submitted papers will include, but are not limited to, extended versions of accepted conference papers in ICAUS 2025, which will be held on 17–19 October 2025 in Shanghai, China. The maximum percentage of overlap will be 30%. Authors must reference the corresponding conference papers. The authors of extended versions of the accepted conference papers in ICAUC 2025 will potentially enjoy a 20% discount on the Article Processing Charge.

Prof. Dr. Shaorong Xie
Prof. Dr. Yifeng Niu
Prof. Dr. Wenxing Fu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.2	4.0	2014	20.9 Days	CHF 2400	Submit
Automation automation	2.0	4.1	2020	23.4 Days	CHF 1200	Submit
Drones drones	4.8	7.4	2017	20.1 Days	CHF 2600	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit

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Published Papers (10 papers)

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All Aerospace Automation Drones Electronics

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21 pages, 6895 KB  

Open AccessArticle

An Innovative Multi-Scale Feature Fusion Network for Change Detection of Remote Sensing Images

by Shiqi Li, Junyu Wei, Shaojing Su, Zongqing Zhao, Weijia Gao, Zhendong Wang, Yongqi Li and Tao Ou

Electronics 2025, 14(23), 4628; https://doi.org/10.3390/electronics14234628 - 25 Nov 2025

Viewed by 191

Abstract

Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and [...] Read more.

Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and thus still face limitations in accuracy and generalization capability. To overcome these limitations, this paper proposed the MSTAN, which consists of a multi-scale Transformer encoder and a decoder centered on a four-layer ASFF module. The four-layer ASFF module dynamically learns spatially adaptive weights to capture multi-scale semantic information. Comparative experiments demonstrate that MSTAN achieves high-precision change detection. Cross-dataset evaluation experiments demonstrate that MSTAN possesses strong generalization ability. Ablation experiments confirm the effectiveness of the four-layer ASFF module in fusing multi-scale features. Complexity analysis quantifies the computational overhead of the four-layer ASFF module. These results highlight MSTAN’s powerful generalization capability and its promising potential for change detection tasks. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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19 pages, 875 KB  

Open AccessArticle

CogMUS: A Soar-Based Cognitive Framework for Mission Understanding in Multi-UAV Cooperative Operation

by Jiaxin Hu, Tao Wang, Hongrun Wang and Jingshuai Cao

Drones 2025, 9(12), 813; https://doi.org/10.3390/drones9120813 - 24 Nov 2025

Viewed by 285

Abstract

The cooperative operation of multiple Unmanned Aerial Vehicles (multi-UAV) is emerging as a pivotal trend in future complex autonomous systems. To enable accurate mission understanding and efficient collaboration among UAVs in complex, dynamic, and uncertain operational environments, this paper introduces CogMUS, a novel [...] Read more.

The cooperative operation of multiple Unmanned Aerial Vehicles (multi-UAV) is emerging as a pivotal trend in future complex autonomous systems. To enable accurate mission understanding and efficient collaboration among UAVs in complex, dynamic, and uncertain operational environments, this paper introduces CogMUS, a novel cooperative mission understanding framework based on the Soar cognitive architecture. We first construct a mission understanding framework for UAV operations centered around five typical mission categories. Building on this foundation, we design a distributed cognitive model where each UAV is equipped with a Soar agent. This model leverages the synergy of working memory (WM), long-term memory (LTM), and the decision cycle (DC) to achieve key functionalities, including hierarchical mission decomposition, dynamic task allocation, and proactive airspace conflict detection and resolution. Through comprehensive simulation experiments, we validate the performance of the proposed CogMUS framework across key metrics, including task understanding accuracy, cooperative efficiency, and overall task completion rate. The results demonstrate that CogMUS exhibits superior adaptability to diverse scenarios, as well as remarkable scalability and robustness. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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15 pages, 842 KB  

Open AccessArticle

Hierarchical Decision Making-Based Intelligent Game Confrontation on UAV Swarm

by Guannan Chang, Siyuan Ren, Shuna Zhang and Xiaofeng Zhang

Aerospace 2025, 12(12), 1033; https://doi.org/10.3390/aerospace12121033 - 21 Nov 2025

Viewed by 318

Abstract

To address the challenge of decision making in close-range air combat for fixed-wing unmanned air vehicle (UAV) swarms, this paper proposes a distributed Hierarchical Cooperative Soft Actor-Critic with maximum entropy (HC-SAC) framework. A top-level target decision-making and bottom-level maneuvering framework is designed to [...] Read more.

To address the challenge of decision making in close-range air combat for fixed-wing unmanned air vehicle (UAV) swarms, this paper proposes a distributed Hierarchical Cooperative Soft Actor-Critic with maximum entropy (HC-SAC) framework. A top-level target decision-making and bottom-level maneuvering framework is designed to resolve convergence issues in traditional multi-agent reinforcement learning, typically for long mission durations and complex state spaces. Friendly tactics are incorporated into top-level decisions to enhance coordination, with both offensive and defensive sub-policies balanced for swarm confrontations. These policies are trained using the Soft Actor-Critic (SAC) deep reinforcement learning algorithm with specific reward functions, and the effectiveness of this method is verified through 5v5 swarm game confrontation simulations. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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20 pages, 4372 KB  

Open AccessArticle

Cross-Subject Cognitive State Assessment for Unmanned System Operators Based on Brain Functional Connectivity

by Jun Chen, Fanzhou Zhao, Xinyu Zhang, Xiaoyu Hu and Kailun Ji

Drones 2025, 9(11), 808; https://doi.org/10.3390/drones9110808 - 19 Nov 2025

Viewed by 304

Abstract

During the operation of Unmanned Aerial Vehicles (UAVs), the cognitive state of operators is prone to decline, posing a risk to task performance. However, many existing cognitive state assessment methods rely directly on raw electroencephalography (EEG) signals, yet exhibit limited robustness when applied [...] Read more.

During the operation of Unmanned Aerial Vehicles (UAVs), the cognitive state of operators is prone to decline, posing a risk to task performance. However, many existing cognitive state assessment methods rely directly on raw electroencephalography (EEG) signals, yet exhibit limited robustness when applied across different individuals. To address this limitation and leverage the spatial information and inter-electrode relationships effectively captured by brain functional connectivity networks, this paper proposes an assessment method based on functional connectivity networks. Data from ten participants under three cognitive states were used to train and test various models on a per-subject basis, where each participant’s data was partitioned into separate training and testing sets. The results demonstrate that the proposed method achieves a mean recognition accuracy of 98.76% with a variance of 0.0113, representing an improvement of at least 7.01% in accuracy and a reduction of at least 0.0191 in variance compared to conventional approaches. This approach facilitates timely cognitive state identification, thereby enhancing the reliability of human–machine interaction in unmanned systems. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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18 pages, 1288 KB  

Open AccessArticle

Automated UAV Object Detector Design Using Large Language Model-Guided Architecture Search

by Fei Kong, Xiaohan Shan, Yanwei Hu and Jianmin Li

Drones 2025, 9(11), 803; https://doi.org/10.3390/drones9110803 - 18 Nov 2025

Viewed by 450

Abstract

Neural Architecture Search (NAS) is critical for developing efficient and robust perception models for UAV and drone-based applications, where real-time small object detection and computational constraints are major challenges. Existing NAS methods, including recent approaches leveraging large language models (LLMs), often suffer from [...] Read more.

Neural Architecture Search (NAS) is critical for developing efficient and robust perception models for UAV and drone-based applications, where real-time small object detection and computational constraints are major challenges. Existing NAS methods, including recent approaches leveraging large language models (LLMs), often suffer from static resource allocation and ambiguous architecture generation, limiting their effectiveness in dynamic aerial scenarios. In this study, we propose PhaseNAS, an adaptive LLM-driven NAS framework designed for drone perception tasks. PhaseNAS dynamically adjusts LLM capacity across exploration and refinement phases, and introduces a structured template language to bridge natural language prompts with executable model code. We also develop a zero-shot detection score for rapid screening of candidate YOLO-based architectures without full training. Experiments on NAS-Bench-Macro, CIFAR-10/100, COCO, and VisDrone2019 demonstrate that PhaseNAS consistently discovers superior architectures, reducing search time by up to 86% while improving accuracy and resource efficiency. On UAV detection benchmarks, PhaseNAS yields YOLOv8 variants with higher mAP and reduced computational cost, highlighting its suitability for real-time onboard deployment. These results indicate that PhaseNAS offers a practical and generalizable solution for autonomous AI model design in next-generation UAV systems. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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19 pages, 3299 KB  

Open AccessArticle

GPLVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Consistent State Estimation with Point and Line Features for Unmanned Aerial Vehicles

by Xinyu Chen, Shuaixin Li, Ruifeng Lu and Xiaozhou Zhu

Drones 2025, 9(11), 801; https://doi.org/10.3390/drones9110801 - 17 Nov 2025

Viewed by 322

Abstract

The employment of linear features to enhance the positioning precision and robustness of point-based VIO (visual-inertial odometry) has attracted mounting attention, especially for UAV (unmanned aerial vehicle) applications where reliable 6-DoF pose estimation is critical for autonomous navigation, mission execution, and safety. This [...] Read more.

The employment of linear features to enhance the positioning precision and robustness of point-based VIO (visual-inertial odometry) has attracted mounting attention, especially for UAV (unmanned aerial vehicle) applications where reliable 6-DoF pose estimation is critical for autonomous navigation, mission execution, and safety. This paper presents GPLVINS—GNSS (global navigation satellite system)-point-line-visual-inertial navigation system—a UAV-tailored enhancement of the nonlinear optimization-based GVINS (GNSS-visual-inertial navigation system). Unlike GVINS, which struggles with feature extraction in weak-texture environments and depends entirely on point features, GPLVINS innovatively integrates line features into its state optimization framework to enhance robustness and accuracy. While existing studies adopt the LSD (line segment detector) algorithm for line feature extraction, this approach often generates numerous short line segments in real-world scenes. Such an outcome not only increases computational costs but also degrades pose estimation performance. In order to address this issue, the present study proposes an NMS (non-maximum suppression) strategy for the refinement of LSD. The line reprojection residual is then formulated as the distance between point and line, which is incorporated into the nonlinear optimization process. Experimental validations on open-source datasets and self-collected UAV datasets across indoor, outdoor, and indoor–outdoor transition scenarios demonstrate that GPLVINS exhibits superior positioning performance and enhanced robustness for UAVs in environments with feature degradation or drastic lighting intensity variations. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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21 pages, 23269 KB  

Open AccessArticle

Wavelet-Guided Zero-Reference Diffusion for Unsupervised Low-Light Image Enhancement

by Yuting Peng, Xiaojun Guo, Mengxi Xu, Bing Ding, Bei Sun and Shaojing Su

Electronics 2025, 14(22), 4460; https://doi.org/10.3390/electronics14224460 - 16 Nov 2025

Viewed by 476

Abstract

Low-light image enhancement (LLIE) remains a challenging task due to the scarcity of paired training data and the complex signal-dependent noise inherent in low-light scenes. To address these issues, this paper proposes a fully unsupervised framework named Wavelet-Guided Zero-Reference Diffusion (WZD) for natural [...] Read more.

Low-light image enhancement (LLIE) remains a challenging task due to the scarcity of paired training data and the complex signal-dependent noise inherent in low-light scenes. To address these issues, this paper proposes a fully unsupervised framework named Wavelet-Guided Zero-Reference Diffusion (WZD) for natural low-light image restoration. WZD leverages an ImageNet-pre-trained diffusion prior and a multi-scale representation of the Discrete Wavelet Transform (DWT) to restore natural illumination from a single dark image. Specifically, the input low-light image is first processed by a Practical Exposure Corrector (PEC) to provide an initial robust luminance baseline. It is then converted from the RGB to the YCbCr color space. The Y channels of the input image and the current diffusion estimate are decomposed into four orthogonal sub-bands—LL, LH, HL, and HH—and fused via learnable, step-wise weights while preserving structural integrity. An exposure control loss and a detail consistency loss are jointly employed to suppress over/under-exposure and preserve high-frequency details. Unlike recent approaches that rely on complex supervised training or lack physical guidance, our method integrates wavelet guidance with a zero-reference learning framework, incorporates the PEC module as a physical prior, and achieves significant improvements in detail preservation and noise suppression without requiring paired training data. Comprehensive experiments on the LOL-v1, LOL-v2, and LSRW datasets demonstrate that WZD achieves a superior or competitive performance, surpassing all referenced unsupervised methods. Ablation studies confirm the critical roles of the PEC prior, YCbCr conversion, wavelet-guided fusion, and the joint loss function. WZD also enhances the performance of downstream tasks, verifying its practical value. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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20 pages, 3683 KB  

Open AccessArticle

Auction- and Pheromone-Based Multi-UAV Cooperative Search and Rescue in Maritime Environments

by Wenqing Zhang, Gang Chen and Zhiwei Yang

Drones 2025, 9(11), 794; https://doi.org/10.3390/drones9110794 - 14 Nov 2025

Viewed by 410

Abstract

Unmanned aerial vehicles (UAVs) play an increasingly vital role in maritime search and rescue (SAR) because they can be deployed quickly, cover large ocean areas, and operate without exposing human crews to risk. Compared with single platforms, multi-UAV cooperation improves efficiency in locating [...] Read more.

Unmanned aerial vehicles (UAVs) play an increasingly vital role in maritime search and rescue (SAR) because they can be deployed quickly, cover large ocean areas, and operate without exposing human crews to risk. Compared with single platforms, multi-UAV cooperation improves efficiency in locating drifting targets influenced by wind and currents. However, existing allocation methods often focus only on immediate task benefits and neglect search history, leading to redundant revisits and lower overall efficiency. To address this problem, we propose a hybrid auction–pheromone framework for multi-UAV maritime SAR. The method combines an auction-based allocation strategy, which assigns tasks according to target probability, distance, and UAV workload, with a pheromone-guided mechanism that records visitation history through exponential decay to discourage repeated searches. A layered model is constructed, consisting of an airspace/weather constraint layer, a target probability layer, a pheromone layer, and a UAV motion layer. UAVs adopt A* path planning with a nearest-first policy, while a stagnation detector triggers dynamic reallocation when coverage slows. Simulation experiments verify the effectiveness of the proposed approach. Compared with auction-only and pheromone-only baselines, the hybrid method reduces the required steps by up to 27.1%, decreases the overlap ratio to 0.135–0.164, and increases the coverage speed by 64.7%. These results demonstrate that integrating explicit auctions with implicit pheromone memory significantly enhances scalability, robustness, and efficiency in multi-UAV maritime SAR. Future research will focus on dynamic drift modeling, real-world deployment, and heterogeneous UAV collaboration. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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13 pages, 10011 KB  

Open AccessArticle

High-Accuracy Rocket Landing via Lossless Convexification

by Wei Xiao, Bei Hong, Junpeng Liu, Xiaofei Chang and Wenxing Fu

Aerospace 2025, 12(11), 1009; https://doi.org/10.3390/aerospace12111009 - 12 Nov 2025

Viewed by 304

Abstract

With the development of rocket technology, achieving high-precision landing has become a key technical challenge in the field of aerospace. To cope with this challenge, we propose a lossless convexification algorithm based on the integral pseudospectral method in this paper. Firstly, for the [...] Read more.

With the development of rocket technology, achieving high-precision landing has become a key technical challenge in the field of aerospace. To cope with this challenge, we propose a lossless convexification algorithm based on the integral pseudospectral method in this paper. Firstly, for the fuel optimization problem, the continuous dynamic equations and constraints are discretized with high accuracy using an integral-type pseudospectral method. By constructing a global integration matrix at Legendre–Gauss nodes, the original complex continuous problem is effectively transformed into a discrete form that is more tractable for numerical optimization. Secondly, the non-convex constraints are transformed using the lossless convexification technique, thereby reformulating the original problem as a second-order cone programming (SOCP) problem. The effectiveness of the proposed algorithm is validated through numerical simulations, which demonstrate high landing accuracy, robustness, and fuel efficiency. These results highlight the algorithm’s high performance and strong potential for practical application in space missions. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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40 pages, 10740 KB  

Open AccessArticle

Structural Design of an Unmanned Aerial Underwater Vehicle with Coaxial Twin Propellers and the Numerical Simulation of the Cross-Domain Characteristics

by Jiancheng Wang, Yikun Feng, Guoqing Zhang, Qiqian Ge, Haobin Jin and Zhewei Zhang

Drones 2025, 9(11), 766; https://doi.org/10.3390/drones9110766 - 6 Nov 2025

Viewed by 608

Abstract

This paper addresses the structural adaptability and dynamic stability challenges faced by unmanned aerial underwater vehicle (UAUV) during the transition between air and water. To overcome these issues, this paper innovatively proposes a UAUV that uses coaxial twin propellers for propulsion and conducts [...] Read more.

This paper addresses the structural adaptability and dynamic stability challenges faced by unmanned aerial underwater vehicle (UAUV) during the transition between air and water. To overcome these issues, this paper innovatively proposes a UAUV that uses coaxial twin propellers for propulsion and conducts a detailed overall structural design and subsystem design for it. Accurate prediction of the kinematic characteristics of UAUV during cross-domain motion is of great significance for the design of high-performance UAUVs. Therefore, a numerical simulation method for UAUV cross-domain motion based on the STAR CCM+ (version 202402) software, the volume of fluid (VOF) method, and the dynamic fluid body interaction (DFBI) module was established. The results showed that when the water-entry speed is small, as the water-entry angle increases, the UAUV’s movement trajectory will exhibit continuous undulating motion. Moreover, during the water-exit process, the smaller the water-exit speed and angle, the greater the change in attitude. The analysis of the dynamic characteristics of cavitation during the UAUV’s water-entry process reveals that the premature rupture of the cavities is detrimental to the UAUV’s movement along the initial entry direction. During the process of the UAUV’s exit from the water, the detachment of water adhering to the UAUV surface will cause certain disturbances to its attitude. The findings of this study provide key theoretical insights and technical references for optimizing the structural design of UAUVs. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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