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A review of cooperative path planning of an unmanned aerial vehicle group

Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1671-1694页

作者： Hao ZHANG Bin XIN Li-hua DOU Jie CHEN Kaoru HIROTA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China Department of Computational Intelligence and Systems Science Tokyo Institute of TechnologyTokyo 1528550Japan

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.

关键词： Unmanned aerial vehicle group Cooperation Path planning Optimization problem

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Feature refinement attention U-net for enhanced liver segmentation

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Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Wang, Hui Liu, Desheng Zhao, Zhilei Wu, Yue Cekderi, Anil Baris State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China College of Information Science & Electronic Technology Jiamusi University Jiamusi154007 China

Developing efficient and accurate automated liver segmentation methods is of far-reaching significance for the prevention, diagnosis, treatment, and prognostic assessment of liver diseases. Artificial intelligence (AI) technology has shown immense potential in various computer vision tasks and has been increasingly adopted in medical image segmentation in recent years. However, conventional neural networks often exhibit limitations in capturing global context capture, constraining their performance in medical image segmentation. To address this challenge, we propose a Feature Refinement Attention U-Net (FRAttU-Net). The architecture is designed with two key components: a High and Low Frequency Feature Fusion and Enhancement Module (HLF3EM) between the encoder and decoder to integrate the multilevel features and enhance the high-level feature representation using edge cues, and an Otsu Density Refinement Module in the output layer to strengthen density information representation and improve the prediction accuracy. Extensive experiments demonstrate that FRAttU-Net achieves state-of-the-art accuracy, outperforming existing methods on multiple datasets. This performance advancement holds considerable potential in improving clinical decision-making in liver disease diagnosis and treatment planning. The code is available at: https://***/HUI623/FRAttU-Net. © 2025 Elsevier Ltd

关键词： Image segmentation

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Drone Image Stitching Using Local Least Square Alignment

Drone Image Stitching Using Local Least Square Alignment

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qi Wan Linbo Luo Jun Chen Yong Wang Donghai Guo School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This paper proposes a strategy for drone image stitching using local least square alignment, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using simple homography cannot handle the situation that the input drone images have parallax effect, and the mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following two steps, namely, local least square alignment and global similarity constraint. Starting from initial feature sets obtained by traditional feature extraction methods, we construct a robust alignment energy based on parallax errors to adaptively eliminate parallax effects. The energy can be efficiently minimized used least square estimate. Combined with global similarity constraint, our proposed strategy can flexibly improve the naturalness of the results. Experiments show that our stitching strategy can more effectively eliminate parallax effects and achieve natural-looking results compared to other state-of-the-art methods.

关键词： Drones Image stitching Distortion Feature extraction Optimization Geology Adaptation models

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Trajectory Tracking Strategy for Gliding Hypersonic Vehicle with Aileron Stuck at an Unknown Angle

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Journal of Beijing Institute of Technology 2019年第3期28卷 447-455页

作者： Haibo Ji Lei Wang School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Ministry of EducationBeijing 100081China

A nonlinear robust trajectory tracking strategy for a gliding hypersonic vehicle with an aileron stuck at an unknown position is presented in this paper. First, the components of translational motion dynamics perpendicular to the velocity are derived, and then a guidance law based on a time-varying sliding mode method is used to realize trajectory tracking. Furthermore, the rotational equations of motion are separated into an actuated subsystem and an unactuated subsystem. And an adaptive time-varying sliding mode attitude controller is proposed based on the actuated subsystem to track the command attitude and the tracking performance and robustness are therefore enhanced. The proposed guidance law and attitude controller make the hypersonic vehicle fly along the reference trajectory even when the aileron is stuck at an unknown angle. Finally, a hypersonic benchmark platform is used to demonstrate the effectiveness of the proposed strategy.

关键词： trajectory tracking stuck aileron time-varying sliding mode

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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Analysis and circuit implementation of a new fourth-order multi-wing chaotic system based on memristors

Analysis and circuit implementation of a new fourth-order mu...

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Chinese automation Congress (CAC)

作者： Lei Jia Xiaowei Jiang Xianhe Zhang Ming Chi Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education School of Automation China University of Geosciences Wuhan P. R. China School of Electrical Engineering and Automation Hubei Normal University Huangshi P. R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781665426480

In this paper, a new fourth-orders memristor chaotic system is obtained by introducing a smooth memristor model as the feedback term based on the modified three-orders chaotic system. The system can generate double-wing, four-wing chaos or hyperchaos for different parameters and initial conditions. Through the analysis of Lyapunov exponent and other dynamics, we can see that the system has complex chaotic, hyperchaotic, and continuous chaotic. Other dynamical behaviors such as multi-stability and extreme multi-stability are found by comparing phase and bifurcation diagrams, and equilibrium point calculations. Finally, the chaotic behavior is successfully verified by circuit simulation.

关键词： Chaos automation Circuit simulation Buildings Memristors Bifurcation Encryption

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Distributed Solving Sylvester equations with an explicit exponential Convergence

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IFAC-PapersOnLine 2020年第2期53卷 3260-3265页

作者： Songsong Cheng Xianlin Zeng Yiguang Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 Beijing China

This paper addresses distributed achieving the least squares solution of Sylvester equations in the form of AX + XB = C. By decomposing the parameter matrices A, B and C, we formulate the problem of distributed solving Sylvester equations as a distributed optimization model and propose a continuous-time algorithm from the primal-dual viewpoint. Then, by constructing a Lyapunov function, we prove that the proposed algorithm can achieve a least squares solution of Sylvester equations with an explicit exponential convergence rate. Additionally, we illustrate the convergence performance by using a numerical example.

关键词： Sylvester equations Distributed optimization Least squares solution exponential convergence

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Analytic Hierarchy Process（AHP） in Portfolio Selection Based on Information Granularity

Analytic Hierarchy Process（AHP） in Portfolio Selection Bas...

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第32届中国控制与决策会议

作者： Kaixin Zhao Yaping Dai Ye Ji Jiayi Sun School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Analytic Hierarchy Process（AHP） is a multi criteria decision-making method,which can describe and transform the qualitative problems quantitatively,and then get the quantitative analysis results in accordance with the causal relationship between decision *** this paper,a granular Analytic Hierarchy Process,which introduces the granularity mechanism,is proposed to solve the portfolio selection problem under the mean-risk *** the proposed method,the scale value of scheme layer is no longer limited to nine positive integers from 1 to 9,which gives granularity attributes to the comparison of advantages and disadvantages in a specific criterion layer between different *** proposed method reflects small differences between different alternative schemes through granularity attribute,so it can provide rich decision information for decision *** numeric examples from the real-world financial market（China Shanghai Stock Exchange） are provided to illustrate an essence of the proposed method.

关键词： Analytic Hierarchy Process（AHP） Portfolio Selection Information Granularity Decision-making

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DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuzzy Clustering Parameters

DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuz...

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第三十九届中国控制会议

作者： Jiayi Sun Yaping Dai Kaixin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

As an interdisciplinary of fuzzy theory and clustering, Fuzzy C-Means（FCM） is widely applied for identifying categories with unlabeled data. However, its application to data which is hard to visualize rises the difficulty for users to determine the input parameters, especially for the number of clusters. In this paper, a kind of fuzzy clustering algorithm with self-regulated parameters named Density-Based Fuzzy C-Means（DBFCM） is proposed by integrating the idea of Density-Based Spatial Clustering of Application with Noise（DBSCAN） into FCM. Its advantage is using the inherit density characteristic of input data to self-determine the parameters of fuzzy clustering. The experimental results demonstrate that the proposed DBFCM can not only self-determine the proper parameters, but also accelerate the convergence process compared to the original FCM.

关键词： Machine Learning Fuzzy Clustering Fuzzy C-Means Parameter Self-Regulation

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Motion Planning of Six-DOF Arm Robot Based on Improved DDPG Algorithm

Motion Planning of Six-DOF Arm Robot Based on Improved DDPG ...

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第三十九届中国控制会议

作者： Zhuang Li Hongbin Ma Yazhe Ding Chen Wang Ying Jin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper presents an improved deep deterministic policy gradient algorithm based on a six-DOF(six multi-degree-offreedom) arm robot. First, we build a robot model based on the DH(Denavit-Hartenberg) parameters of the UR5 arm robot. Then,we improved the experience pool of the traditional DDPG(deep deterministic policy gradient) algorithm by adding a success experience pool and a collision experience pool. Next, the reward function is improved to increase the degree of successful reward and the penalty of collision. Finally, the training is divided into segments, the front three axes are trained first, and then the six axes. The simulation results in ROS(Robot Operating System) show that the improved DDPG algorithm can effectively solve the problem that the six-DOF arm robot moves too far in the configuration space. The trained model can reach the target area in five steps. Compared with the traditional DDPG algorithm, the improved DDPG algorithm has fewer training episodes,but achieves better results.

关键词： Motion Planning Improved DDPG Six-DOF Arm Robot ROS

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