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Targets capture by distributed active swarms via bio-inspired reinforcement learning

Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 206-217页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common *** from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific ***,we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement *** exploit collective milling structures of natural herds to cage the targets,and adopt a switching control policy inspired by sperms’chiral dynamics to optimize the trajectories of individuals,through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical,adaptive and scalable *** exists a critical swarm size,beyond which the excessive competition between agents would generate large mechanical forces,leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple *** work provides physical insights into distributed active swarms and could offer a multilevel,decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices,machine immunity,and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Impulsive thrust strategy for orbital pursuit-evasion games based on impulse-like constraint

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Chinese Journal of Aeronautics 2025年第1期38卷 520-536页

作者： Hongbo WANG Yao ZHANG Hao LIU Kunpeng ZHANG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China National Key Laboratory of Space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China

This paper proposes a novel impulsive thrust strategy guided by optimal continuous thrust strategy to address two-player orbital pursuit-evasion game under impulsive thrust *** strategy seeks to enhance the interpretability of impulsive thrust strategy by integrating it within the framework of differential game in traditional continuous ***,this paper introduces an impulse-like constraint,with periodical changes in thrust amplitude,to characterize the impulsive thrust ***,the game with the impulse-like constraint is converted into the two-point boundary value problem,which is solved by the combined shooting and deep learning method proposed in this *** learning and numerical optimization are employed to obtain the guesses for unknown terminal adjoint variables and the game terminal ***,the accurate values are solved by the shooting method to yield the optimal continuous thrust strategy with the impulse-like ***,the shooting method is iteratively employed at each impulse decision moment to derive the impulsive thrust strategy guided by the optimal continuous thrust *** examples demonstrate the convergence of the combined shooting and deep learning method,even if the strongly nonlinear impulse-like constraint is *** effect of the impulsive thrust strategy guided by the optimal continuous thrust strategy is also discussed.

关键词： Orbital pursuit-evasion game Differential game Impulsive thrust Deep learning Shooting method

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Observer-Based Multi-Agent Reinforcement Learning for Pursuit-Evasion Game With Multiple Unknown Uncertainties

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14332-14345页

作者： Liu, Yangyang Liu, Chun Meng, Yizhen Jiang, Bin Wang, Xiaofan Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University School of Mechatronic Engineering and Automation Institute of Artificial Intelligence Shanghai200444 China Shanghai Aerospace Control Technology Institute National Key Laboratory of Space Target Awareness Shanghai China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing210016 China Shanghai Institute of Technology Shanghai201418 China

This paper aims to investigate the challenging problem of a multi-agent game with multiple pursuers and a single evader in an environment with multiple unknown uncertainties. A coupled approach combining decentralized observers and reinforcement learning (RL) controllers is proposed to deal with this scenario. Firstly, decentralized observers driven by auxiliary control laws are introduced to estimate the states of uncertain systems, with their best responses obtained through the adaptive dynamic programming (ADP) method. The estimated states, which reflect the actual states of the pursuers’ systems, are concurrently transmitted to the RL controllers. Subsequently, the controllers are trained with observer-based heterogeneous-agent proximal policy optimization (OHAPPO) algorithm, in which a novel global multi-function cost is designed. The algorithm utilizes the advantage decomposition for policy updates in the way of credit assignment, resulting in more stable and efficient updates compared to traditional value decomposition. Moreover, to further enhance the performance of both observers and controllers, a sequential game is established between them, where observers’ policies are influenced by controllers’ optimal control and vice versa. Finally, the simulation results verify the effectiveness of the designed OHAPPO algorithm in the pursuit-evasion game. © 2004-2012 IEEE.

关键词： Reinforcement learning

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Reinforcement learning control of collision avoidance for ultra-close formation of spacecraft with input constraints

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2025年第4期42卷 659-668页

作者： Meng, Yi-Zhen Huang, Jing Zhou, Shao-Hui Zhou, Bin Zhu, Kang-Wu Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Shanghai Space Technology Co. Ltd Shanghai201109 China

Considering the control problem of reconstructing the ultra-tight formation of near-Earth orbit spacecraft in the presence of external disturbances, collision avoidance constraints, and fixed-time constraints, this study presents a robust control method for spacecraft formation that accounts for the dead-zone effect of the actuator under multiple constraint conditions. Firstly, we establish the nonlinear dynamic equations governing the relative positions of the spacecraft in the complete near-Earth orbit formation, as well as the dynamic response model capturing the dead-zone effect of the actuator. Secondly, we design a constraint mechanism for the relative positions of the formation based on state constraints. Robust control laws, employing a combination of backstepping and a reinforcement learning actor-critic network, are proposed to address collision avoidance constraints and fixed-time constraints. Additionally, we approximate the deadzone characteristics of the actuator’s thrusters by leveraging a reinforcement learning actor network. To mitigate the impact of the dead-zone effect on control accuracy, we minimize the cost function of the actor network. The Lyapunov stability theorem is employed to demonstrate the uniformly boundedness of the closed-loop system. Finally, we conduct simulation verification on the MATLAB/Simulink platform, and the results substantiate the effectiveness of the proposed method. © 2025 South China University of technology. All rights reserved.

关键词： Cost functions

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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of shanghai Jiaotong University (Science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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Broadband low-frequency sound absorption of multifunctional composite metastructure

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Science China(Technological Sciences) 2025年第2期68卷 114-124页

作者： Yilong YANG Shanshan LIU Yan LI Tong GAO Yongdong PAN Yong LI Yabin JIN School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China State International Joint Research Center of Aerospace Design and Additive Manufacturing Northwestern Polytechnical UniversityXi’an 710072China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Aircraft Mechanics and Control Shanghai 200092China

In this study,we fabricated multifunctional metastructures from carbon fiber-reinforced plastic composites using additive manufacturing *** metastructures are characterized by their lightweight,load-bearing capacity,and broadband low-frequency sound absorption *** metastructure consists of 36 unit cells,and non-local coupling mechanism was considered for designing the sound absorption *** developed an acoustic impedance theory tailored for the metastructure,facilitating an analysis of thermal and viscous dissipation *** is proven theoretically and experimentally that the proposed composite metastructure can achieve a noise reduction with an average sound absorption coefficient greater than 0.9 across frequencies in the rage of 330-1500 *** also studied the metastructure’s quasi-static and cyclic compression performance,confirming its efficient absorption capabilities after cyclic *** proposed design and additive manufacturing method for composite metastructures provides a novel pathway for creating lightweight,multifunctional structures with diverse applications,such as aerospace engineering.

关键词： composite metastructure broadband sound absorption additive manufacturing

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Dynamic Regret for Distributed Online Composite Optimization

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IEEE Transactions on Automatic control 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai201800 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Institute of Intelligent Science and Technology Shanghai201210 China

This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains (Formula presented) dynamic regret bound when each local loss is a general convex composite function, where CT is the path variation. If CT can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of (Formula presented) and o (log T(1 + CT)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains o (1 + CT) dynamic regret bound. In the end, numerical results are given to support the theoretical findings. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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A Two-Stage Payload Dynamic Parameter Identification Method for Interactive Industrial Robots With Large Components

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 13871-13883页

作者： Liu, Mingxuan Li, Pengcheng Duan, Jinjun Liu, Lunqian Shen, Ye Tian, Wei Ji, Yuqi Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering Nanjing210016 China State Key Laboratory of Intelligent Manufacturing System Technology for Complex Products Beijing100854 China Shanghai Aircraft Manufacturing Company Shanghai200120 China Shanghai Aerospace Electronic Technology Research Institute Shanghai201109 China

Taking human-robot collaborative assembly as an example, the methods based on contact forces can improve the assembly efficiency of industrial robots with large components in industrial manufacturing. However, due to the large size, high payload, assembly accuracy and dynamic changes in grip position, accurately estimating the contact forces between the payload and the operator becomes challenging when handling these large components. In this paper, a two-stage method is proposed for payload dynamic parameter identification. The parameter identification equation in the sensor coordinate system is initially established. Furthermore, the identification model of recursive restricted total least squares (RRTLS) based on total least squares (TLS) is constructed to achieve low-consumption online identification. According to the assembly requirements and payload characteristics, the posture coordinate system is designed for safety, including the feasible workspace for the robot. Subsequently, the static identification postures and dynamic excitation trajectory are planned to obtain static values and dynamic inertial parameters. In the end, a high-payload human-robot collaborative assembly system is built to validate the proposed method. Experimental results show that compared with the existing methods, the proposed approach can effectively identify and compensate the payload, leading to more accurate external force sensing. © 2004-2012 IEEE.

关键词： Industrial robots

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Image-Spectrum Collaborative Detecting System for Dynamic Target Detection 10

Image-Spectrum Collaborative Detecting System for Dynamic Ta...

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10th Symposium on Novel Optoelectronic Detection technology and Applications

作者： Gu, Luo Hou, Linbao Lin, Yue Wang, Xiang Yang, Qiujie He, Zhiping Key Laboratory of Space Active Opto-electronic Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Remote detection and identification of small targets has been the significant issue in areas of industry and business. For example, a number of drone invasion affairs happen around the civil airports in recent years, which seriously affect the international shipping and do great damage to international economy even passengers' lives. Detection of the dynamic small target poses a great challenge to detecting equipment. Traditional small target detecting technologies are based on optical imaging, depending on the morphological characteristics of target's image across the focal plane. In other words, traditional detecting and imaging methods work only in the case where the image of small target occupies enough pixels. In the case of dynamic and small target detection, the morphological characteristics of target's image largely degenerate and current technologies are difficult to work well. In this paper, an optical system of image-spectrum collaborative detecting is presented. With ingeniously designed redirection optics integrated, the proposed system allows for the easy detection and identification of the dynamic and small targets. Operating principle of image-spectrum collaborative detecting is elaborated. An imaging channel with field of view no less than 2°is designed at first, and then a spectrum measuring channel is designed and combined with the imaging channel. The imaging channel takes the role in detecting target, and the direction information from the imaging channel is transferred to the spectrum measuring channel. Under the guidance of azimuth and elevation, the system can identify the dynamic and small target by accurately measuring the characteristic spectrum which is referred to as the object's fingerprint. With more sophisticated optical design, a detecting system with large field of view can be obtained. The presented system in this paper shows a great potential in solving the problem of detecting and identifying the dynamic and small targets. © 2025

关键词： Optical design

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