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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Yin, Yizhen Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Liaoning Shenyang China Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9789811694912

In autonomous robot exploration, it is very important that the robot can autonomously plan the exploration path of the unknown environment and create a map representation of the surrounding environment. In the robot's autonomous environment traversal method, one branch is the frontier-based detection method. This method consumes more and more computing resources as the map expands and is not suitable for large-scale environment detection. Another branch is the random exploration method based on multiple rapidly-exploring randomized trees. This method uses the edge of the tree to search for the boundary, but the selection of the next exploration point is very demanding. This paper proposes an exploration point selection strategy based on the hybrid clustering method, which speeds up the RRT exploration method. Simulation experiments show that the improved RRT exploration method proposed in this paper takes less time to explore and build maps, especially in a large-scale mapping environment, which effectively improves the completeness and efficiency of mapping. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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Flexible Physical Camouflage Generation Based on a Differential Approach

arXiv

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arXiv 2024年

作者： Li, Yang Tan, Wenyi Wang, Tingrui Liang, Xinkai Pan, Quan School of Automation Northwestern Polytechnical University Xi’an710072 China Laboratory of Science and Technology on Complex System Control and Intelligent Agent Cooperation Beijing China

To achieve attacks on object detection in the physical world, many existing studies focus on adversarial camouflage generation techniques, which involve applying adversarial textures onto the surface of the target. However, the neural renderers used in previous works cannot realistically simulate the physical world, and most texture rendering processes are implemented on 2D images, without fully considering the influence of the structural information of 3D models on texture overlay. To address these challenges, we introduce a novel neural rendering approach specifically tailored for adversarial camouflage within a versatile 3D rendering framework. Our method, named Flexible Physical-camouflage Attack (FPA), goes beyond traditional techniques by faithfully simulating lighting conditions, material variations of the camouflage, the angle and distance between the target and the camera, ensuring a nuanced and realistic representation of textures on a 3D target. To achieve this, we employ a generative approach that learns adversarial patterns from a diffusion model. This involves combining specially designed adversarial loss, smoothness loss, non-printable score loss, and concealment constraint loss to ensure the adversarial, printability, and concealable nature of camouflage in the physical world. Furthermore, we showcase the effectiveness of the proposed camouflage in sticker mode, demonstrating its ability to cover the target without compromising adversarial information. Through empirical and physical experiments, FPA exhibits strong performance in terms of attack success rate and transferability. Additionally, the designed sticker-mode camouflage, coupled with a concealment constraint, adapts to the environment, yielding diverse styles of texture. Our findings highlight the versatility and efficacy of the FPA approach in adversarial camouflage applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Camouflage

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Semi-autonomous Multicopter Formation control Under One Remote Pilot in GPS-Denied Environment

Semi-autonomous Multicopter Formation Control Under One Remo...

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Gao, Yan Wei, Donghui Bai, Chenggang Quan, Quan School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9789811694912

The multicopter formation is widely used in many different complex circumstances. And the semi-autonomous multicopter formation controlled with a single pilot on the ground draws people’s attention due to the great adaptability to various kinds of environments. In this paper, we focus on the problem of passing through narrow channels with semi-autonomous multicopter formation. Similar to the definition for a single multicopter, the altitude hold mode for the multicopter formation is defined first. Then, the formation decision-making and formation low-level control of the altitude hold mode are proposed one by one. Finally, a hardware-in-the-loop simulation and a real experiment are performed with four multicopters to pass through a narrow channel. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Cross-Domain Guidance Positioning Method Based on Multi-Layer Network Structure 21

Cross-Domain Guidance Positioning Method Based on Multi-Laye...

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3rd European Symposium on Computer and Communications, ESCC 2021

作者： Wu, ChengFeng Guo, XiaoYun Xu, Cheng Qi, Hang Liu, Yanxu Zhao, Jianmin Hu, Enwen Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory China Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450387491

Oriented towards the requirements for reliable positioning and navigation of aircraft under the condition of rejection of navigation satellite, we proposed cross-domain guide positioning methods based on multi-layer network coverage, and designed the relative positioning network construction methods between ground nodes, aircrafts and ground nodes, aircrafts and target nodes. Combined with the relative positioning network characteristics of each layer, at the same time, considered the computational complexity and location accuracy, the self-established multi-point coordinate system method and the robust location method are proposed. © 2021 ACM.

关键词： Aircraft

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Warship Object Detection in Remote Sensing Images with Improved YOLOv5

Warship Object Detection in Remote Sensing Images with Impro...

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Chinese Automation Congress (CAC)

作者： Caiyuan Liu Yuan Li Linxiu Chen Weili Guan School of Automation Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientations, etc. In this paper, we propose a ship rotating object detection network based on the improved YOLOv5, which is constructed through the strategies of introducing Swin Transformer as a feature extraction network to enhance the feature extraction performance of the network, introducing a rotating detection head to realize the detection of the rotation angle, and modifying the network loss function to accelerate the convergence of the network. The network finally achieves a 71.7% map on ShipRSImageNet validation set, which is an improvement of 2.4% compared with the original network model. The network proposed in this paper solves the problem that the YOLOv5 algorithm is unable to detect rotating objects, and the network based on the self-attention mechanism is used to further enhance the ability to detect small objects. Finally, a ship object detector that can be used in real remote sensing satellite images is obtained.

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A belief rule-based fault diagnosis method for Multi-UAVs under missing attribute data

A belief rule-based fault diagnosis method for Multi-UAVs un...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhao, ErKai Hao, MingRui Yang, RuoHan Yue, KeYuan Yu, DeLin Harbin Normal University School of Computer Science and Information Engineering Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Northwestern Polytechnical University School of Mathematics and Statistics Xi'an China

ISBN: (纸本)9798350312492

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation. © 2023 IEEE.

关键词： Failure analysis

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Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data

Entity Recognition for Military Situation Awareness Knowledg...

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Chinese control and Decision Conference, CCDC

作者： Linxiu Chen Weili Guan Xudong Guo Yuan Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Automation Beijing Institute of Technology Beijing China

Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.

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SeqEnhance: A Lightweight Image Processing PipeLine for Low-Light Image Enhancement 7

SeqEnhance: A Lightweight Image Processing PipeLine for Low-...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhou, Runlin Huang, Peiliang Zhang, Dingwen Ren, Jun Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9798350359145

Low-light image enhancement (LLIE) is an important task in computer vision, aiming to improve the visual perception or interpretability of images captured in poorly illuminated environments. Recently, deep learning based methods have been extensively explored to address this issue. While many of these methods have achieved significant advancements in various evaluation metrics for LLIE, only a few have made progress in improving inference speed for the resulting images. As a result, achieving both high-quality enhancements and efficient inference speed remains a challenge. To tackle this challenge, we propose SeqEnhance, a fast LLIE method based on a predefined parameterized image processing pipeline. Our approach combines the inference capabilities of deep neural networks for parameter estimation and the efficient processing capabilities of image processing pipelines to generate enhanced images in an end-to-end manner. The experimental results demonstrate that the proposed method achieves competitive performance on image quality evaluation metrics such as PSNR and SSIM with a fast inference speed. © 2023 IEEE.

关键词： Image enhancement

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Relative navigation method for unmanned aerial vehicles formation using consensus unscented Kalman filter

Relative navigation method for unmanned aerial vehicles form...

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International Symposium on Autonomous systems (ISAS)

作者： Zhixing Zhuang Baichun Gong Xin Ding Mingrui Hao Linxiu Chen Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

A relative navigation method in which the measurement information between aircraft only has distance or angle was proposed to achieve accurate relative navigation between aircraft formations in the global navigation satellite system (GNSS) Denied Environment. In addition, four relative measurement schemes are analyzed. An augmented state is introduced in the relative navigation system model, and the observability analysis of the navigation system is carried out relying on the Lie derivative theory, which gives the limitation of motion: the relative position and velocity of the aircraft cannot be parallel or zero, and a single aircraft cannot keep flying straight. A consensus unscented Kalman filter (CUKF) algorithm is established using the geometric characteristics in flight. The observability of the system state is improved after using the additional constraint. Finally, a standard Monte Carlo simulation system is established, and the system simulation is carried out, which shows that the Observability analysis is verified to be correct, and the algorithm demonstrates high accuracy.

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A transfer learning fusion model for semi-physical simulation and filed accuracy data 3

A transfer learning fusion model for semi-physical simulatio...

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3rd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Geng, Huapin Li, Xiaolu Li, Ziwei Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100083 China Beijing Electro-mechanical Engineering Institute Beijing100083 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783800759330

In order to solve the problem that the accuracy assessment of a single data source is subject to multiple constraints, data fusion reliability evaluation model based on transfer learning was established. Take the high-confidence but small sample field test data as the target domain, the semi-simulation test data as the source domain. Using the principal component analysis to extract the features separately, then embedding the feature distribution in glassman manifold space as two points, construct geodesic flow curve to attach them. So as to achieve data fusion, map the source and target domain to a common space through the geodesic flow curve. Accuracy evaluation can be calculated by constructing confidence region of semi-simulation data. The example verifies the feasibility and effectiveness of this model. © VDE VERLAG GMBH.

关键词： Data fusion

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