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Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

Science China(Technological Sciences) 2024年第2期67卷 435-448页

作者： YUAN GuangSong DUAN HaiBin State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang University(BUAA)Beijing 100083China

This paper proposes a comprehensive design scheme for the extremum seeking control(ESC)of the unmanned aerial vehicle(UAV)close formation *** proposed design scheme combines a Newton-Raphson method with an extended Kalman filter(EKF)to dynamically estimate the optimal position of the following UAV relative to the leading *** reflect the wake vortex effects reliably,the drag coefficient induced by the wake vortex is considered as a performance ***,the performance function is parameterized by the first-order and second-order terms of its Taylor series *** the excellent performance of nonlinear estimation,the EKF is used to estimate the gradient and the Hessian matrix of the parameterized performance *** output feedback of the proposed scheme is determined by iterative calculation of the Newton-Raphson *** with the traditional ESC and the classic ESC,the proposed design scheme avoids the slow continuous time integration of the *** allows a faster convergence of relative position ***,the proposed method can provide a smoother command during the seeking process as the second-order term of the performance function is taken into *** convergence analysis of the proposed design scheme is accomplished by showing that the output feedback is a supermartingale *** improve estimation performance of the EKF,a improved pigeon-inspired optimization(IPIO)is proposed to automatically tune the noise covariance *** Carlo simulations for a three-UAV close formation show that the proposed design scheme is robust to the initial position of the following UAV.

关键词： unmanned aerial vehicle close formation extremum seeking control Newton-Raphson method improved pigeon-inspired optimization

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AR assistance for efficient dynamic target search

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Computational Visual Media 2023年第1期9卷 177-194页

作者： Zixiang Zhao Jian Wu Lili Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina

When searching for a dynamic target in an unknown real world scene,search efficiency is greatly reduced if users lack information about the spatial structure of the *** target search studies,especially in robotics,focus on determining either the shortest path when the target’s position is known,or a strategy to find the target as quickly as possible when the target’s position is ***,the target’s position is often known intermittently in the real world,e.g.,in the case of using surveillance *** goal is to help user find a dynamic target efficiently in the real world when the target’s position is intermittently *** order to achieve this purpose,we have designed an AR guidance assistance system to provide optimal current directional guidance to users,based on searching a prediction *** assume that a certain number of depth cameras are fixed in a real scene to obtain dynamic target’s *** system automatically analyzes all possible meetings between the user and the target,and generates optimal directional guidance to help the user catch up with the target.A user study was used to evaluate our method,and its results showed that compared to free search and a top-view method,our method significantly improves target search efficiency.

关键词： augmented reality(AR) search guidance

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From animal collective behaviors to swarm robotic cooperation

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National Science Review 2023年第5期10卷 82-99页

作者： Haibin Duan Mengzhen Huo Yanming Fan State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang University Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng Laboratory AVIC Shenyang Aircraft Design and Research Institute

The collective behaviors of animals,from schooling fish to packing wolves and flocking birds,display plenty of fascinating phenomena that result from simple interaction rules among *** emergent intelligent properties of the animal collective behaviors,such as self-organization,robustness,adaptability and expansibility,have inspired the design of autonomous unmanned swarm *** article reviews several typical natural collective behaviors,introduces the origin and connotation of swarm intelligence,and gives the application case of animal collective *** this basis,the article focuses on the forefront of progress and bionic achievements of aerial,ground and marine robotics swarms,illustrating the mapping relationship from biological cooperative mechanisms to cooperative unmanned cluster ***,considering the significance of the coexisting-cooperative-cognitive human-machine system,the key technologies to be solved are given as the reference directions for the subsequent exploration.

关键词： collective behaviors swarm intelligence cooperative robotics swarm human-machine system

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Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

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Chinese Journal of Aeronautics 2024年第5期37卷 27-38页

作者： Tongyan WU Haibin DUAN Yanming FAN State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China AVIC Shenyang Aircraft Design and Research Institute Shenyang 110035China

Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and *** detection based on image intrinsic cues can achieve fast detection,but with poor *** studies reveal that optimization-based methods provide accurate and quick solutions for saliency *** paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue *** can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is presented to provide better precision and *** results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic *** algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

关键词： Autonomous aerial refueling Drones Hybrid pigeon-inspired optimization Color opponent Saliency detection Saliency aggregation

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kLCRNet: Fast Road Network Extraction Via keypoint-Driven Local Connectivity Exploration

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12074-12089页

作者： Zhang, Mingming Wang, Bin Yang, Shuai Liu, Qingjie Wang, Yunhong Beihang University Hangzhou Innovation Institute Hangzhou310051 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China

Road network extraction from remote sensing images has been extensively studied in recent decades. While many approaches output road networks in vector format, most are not fully end-to-end, requiring time-consuming post-processing steps. Additionally, challenges like isomorphic encoding limit the flexibility of these methods. In this paper, we present kLCRNet, an efficient road network extraction framework that overcomes these limitations by leveraging keypoint-driven local connectivity exploration. kLCRNet consists of two key components: a keypoint detection (KPD) module that identifies road keypoints via heatmap-based detection and refines them using bipartite matching, and a local connectivity exploration (LCE) module that samples local connection relationships to directly construct connectivity between detected keypoints. Experiments on the CityScale and SpaceNet datasets demonstrate that kLCRNet outperforms state-of-the-art (SOTA) methods in topological accuracy and connectivity. Additionally, kLCRNet significantly improves inference speed by up to 25 times, highlighting its efficiency and effectiveness. © 2008-2012 IEEE.

关键词： Roads Accuracy Remote sensing Feature extraction Vectors Iterative methods Image segmentation Semantic segmentation Encoding Decoding

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DAU-Net: A medical image segmentation network combining the Hadamard product and dual scale attention gate

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Mathematical Biosciences and Engineering 2024年第2期21卷 2753-2767页

作者： Zhang, Xiaoyan He, Mengmeng Li, Hongan School of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Medical image segmentation has an important application value in the modern medical field, it can help doctors accurately locate and analyze the tissue structure, lesion areas, and organ boundaries in the image, which provides key information support for clinical diagnosis and treatment, but there are still a large number of problems in the accuracy of the segmentation, so in this paper, we propose a medical image segmentation network combining the Hadamard product and dual-scale attention gate (DAU-Net). First, the Hadamard product is introduced in the structure of the fifth layer of the codec for element-by-element multiplication, which can generate feature representations with more representational capabilities. Second, in the jump connection module, we propose a dual scale attention gating (DSAG), which can highlight more valuable features and achieve more efficient jump connections. Finally, in the decoder feature structure, the final segmentation result is obtained by aggregating the feature information provided by each part, and decoding is achieved by up-sampling operation. Through experiments on two public datasets, Luna and Isic2017, DAU-Net is able to extract feature information more efficiently using different modules and has better segmentation results compared to classical segmentation models such as U-Net and U-Net++, and also verifies the effectiveness of the model. © 2024 American institute of Mathematical Sciences. All rights reserved.

关键词： Image segmentation

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Multi-task learning andjoint refinement between camera localization and object detection

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Computational Visual Media 2024年第5期10卷 993-1011页

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518052China Qingdao Research Institute of Beihang University Qingdao 266104China School of Computer Science and Technology Shandong UniversityQingdaoChina

Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.

关键词： visual localization object detection joint optimization multi-task learning

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Robust and efficient edge-based visual odometry

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Computational Visual Media 2022年第3期8卷 467-481页

作者： Feihu Yan Zhaoxin Li Zhong Zhou State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Institute of Computing Technology Chinese Academy ofSciencesBeijing 100190China

Visual odometry,which aims to estimate relative camera motion between sequential video frames,has been widely used in the fields of augmented reality,virtual reality,and autonomous ***,it is still quite challenging for stateof-the-art approaches to handle low-texture *** this paper,we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera *** contrast to direct methods,we choose reprojection error to construct the optimization energy,which can effectively cope with illumination *** distance transform map built upon edge detection for each frame is used to improve tracking efficiency.A novel weighted edge alignment method together with sliding window optimization is proposed to further improve the *** on public datasets show that the method is comparable to stateof-the-art methods in terms of tracking accuracy,while being faster and more robust.

关键词： visual odometry(VO) edge structure distance transform low-texture

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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Fish Detection and Quantity Estimation Based on Sonar Images 1st

Fish Detection and Quantity Estimation Based on Sonar Images

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1st International Conference on Extended reality, ICXR 2024

作者： Du, Wenxiang Yan, Shuai Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Qingdao Research Institute of Beihang University Qingdao266100 China

ISBN: (纸本)9789819636785

Due to the complex underwater environment and dense targets, it is difficult to quickly and accurately calculate the location and quantity of fish targets in marine ranching. We have developed an underwater target detection and counting algorithm based on sonar images, which includes four parts: image preprocessing, background subtraction, contour detection, and target counting. Firstly, we design a sliding-window-based gain algorithm based on the uneven grayscale value of sonar images, which amplifies the effective signal while smoothing the grayscale of the image. Secondly, background subtraction is used to separate the foreground and background of the sonar image, which can remove background noise and filter the target using a filtering algorithm to generate a binary image with clear targets. Then, target detection is combined with morphological processing techniques and contour detection algorithms. Finally, we use image erosion technology to separate overlapping targets, calculate the contour position of the targets, and count the quantity. A large number of results indicate that our algorithm can quickly and accurately detect the position of fish in sonar images while also counting the number of fishes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary images

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