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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Zhang, Wenbo Yu, Boxiao Tong, Jiahui Li, Jing Beijing Electro-Mechanical Engineering Institute National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331527624

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement. © 2024 IEEE.

关键词： Decision-making Dynamic Bayesian networks Equipment Simulation Uncertainty Management

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Distributed Event-Triggered Cooperative Formation control of Fixed-Wing UAV 1

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Yue, Keyuan Yuan, Jianquan Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789811694929

ISBN: (纸本)9789811694912

In order to save the communication bandwidth between the fixed-wing unmanned aerial vehicles (UAVs), a cooperative formation control algorithm based on event-triggered communication mechanism is proposed in this paper. The aircraft uses a state estimator to estimate the states of other aircrafts, and when the state of the aircraft itself exceeds a certain threshold, its state information is sent to the neighboring aircraft through the data link, so that the neighboring aircraft updates the states of its estimator. Taking the established autopilot model with a 6-dof inner-loop attitude stabilization loop as the formation control object, a multi-aircraft outer-loop formation controller is designed based on the consensus algorithm. Through numerical simulation, under the condition of ensuring certain control performance of the aircraft cluster, the communication between aircrafts is reduced and bandwidth resources are saved, which verifies the effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fixed wings

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Design of Cooperative Combat Task Architecture for Unknown Wide Area Cluster Reconnaissance and Strike

Design of Cooperative Combat Task Architecture for Unknown W...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Bu, Yunong Chi, Qingxi Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811581540

In the face of the requirements of information warfare in the future battlefield under the condition of high-intensity confrontation, cluster warfare has become the main trend of the development of some weapons and equipment. This makes us need to explore the cooperative operation mechanism of weapon equipment cluster, realize the rapid response of the cluster to the change of battlefield situation under the uncertain operation environment, complete the OODA cycle of operation, and improve the autonomous operation ability of the cluster. This paper takes the unknown wide area multi-target cooperative reconnaissance and strike task as the operational scenario, combs the task flow, function composition, network structure and data interaction relationship. Based on the task constraint, the architecture of cluster cooperative combat task is designed. The cooperative OODA closed-loop is preliminarily realized. It provides the basis for further exploring the overall technology of cooperative combat mission architecture. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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A Survey of Cooperative Path Planning for Multiple UAVs

A Survey of Cooperative Path Planning for Multiple UAVs

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Sun, Wendi Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100074 China

ISBN: (纸本)9789811694912

Cooperative mission for Multi UAVs has become an important trend. Cooperative path planning is one of the most killing part for the issue. The algorithms can be divided into the following categories, including optimal algorithm, graph-theory-based planning method, heuristic-information-based planning algorithm, swarm intelligence algorithm and neural network algorithm and so on. This paper systematically presents the research status and trend of the application of path planning in the field of multi-UAV cooperative mission and selects a variety of classic and up-to-date algorithms and their variants to promote future research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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A credibility allocation approach of complex simulation systems 35

A credibility allocation approach of complex simulation syst...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Zhang, Huan Li, Wei Geng, HuaPin Ma, Ping Yang, Ming Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9788885741881

complex simulation systems usually need to satisfy the credibility requirements and the credibility of complex simulation systems is inextricably linked to the credibility of simulation sub-systems. Hence, in order to guarantee the credibility of complex simulation systems meets requirements, it is necessary to allocate the credibility of simulation sub-systems. complex simulation systems are composed of several correlative simulation sub-systems and their credibility has correlation relationships, which makes it difficult to obtain the credibility of simulation sub-systems quickly. The existing works mostly ignore the relationships between the sub-systems and are difficult to generate the credibility allocation results of simulation sub-systems. In this paper, we model the credibility allocation problem as a probabilistic inference problem based on the pairwise Markov random field (PMRF). In addition, we apply several inference algorithms to perform the inference exactly and approximately in the credibility PMRF. Finally, experiments are conduted on the air defense combat complex simulation system, and the experiment results demonstrate that the proposed method can obtain the credibility allocation results quickly and accurately. © 2023 The Authors.

关键词： Inference engines

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Improving performance of robots using human-inspired approaches: a survey

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science China(Information sciences) 2022年第12期65卷 5-35页

作者： Hong QIAO Shanlin ZHONG Ziyu CHEN Hongze WANG State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artifcial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Realizing high performance of ordinary robots is one of the core problems in robotic research. Improving the performance of ordinary robots usually relies on the collaborative development of multiple research fields, resulting in high costs and difficulty to complete some high-precision tasks. As a comparison,humans can realize extraordinary overall performance under the condition of limited computational-energy consumption and low absolute precision in sensing and controlling each body unit. Therefore, developing human-inspired robotic systems and algorithms is a promising avenue to improve the performance of robotic systems. In this review, the cutting-edge research work on human-inspired intelligent robots in decisionmaking, cognition, motion control, and system design is summarized from behavior-and neural-inspired aspects. This review aims to provide a significant insight into human-inspired intelligent robots, which may be beneficial for promoting the integration of neuroscience, machinery, and control, so as to develop a new generation of robotic systems.

关键词： human-inspired intelligent robots brain-inspired intelligence decision making visual cognition musculoskeletal robots

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Fixed-Time Consensus-Based Nash Equilibrium Seeking

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 267-269页

作者： Mengwei Sun Jian Liu Lu Ren Changyin Sun the Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China IEEE the School of Automation and the Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the School of Artificial Intelligence and the Engineering Research Center of Autonomous Unmanned System Technology Ministry of EducationAnhui UniversityHefei 230601China

Dear Editor,This letter examines the fixed-time stability of the Nash equilibrium(NE)in non-cooperative *** propose a consensus-based NE seeking algorithm for situations where players do not have perfect information and communicate via a topology *** proposed algorithm can achieve NE in a fixed time that does not depend on initial conditions and can be adjusted in advance.

关键词： Fixed seeking letter

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Infrared small target detection algorithm with complex background based on YOLO-NWD 22

Infrared small target detection algorithm with complex backg...

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4th International Conference on Image Processing and Machine Vision, IPMV 2022

作者： Zhou, Xiao Jiang, Lang Guan, Xujun Mou, Xingang Wuhan University Of Technology China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory China

ISBN: (纸本)9781450395823

Because of small number of occupied pixels, lacking shape and texture information, the reliability of infrared remote target detection has always been a difficult research topic. To improve the accuracy and precision of detection of infrared small targets under complex background conditions, a deep learning-based infrared small target detection algorithm YOLO-NWD is proposed. According to the characteristics of small and medium targets in infrared images, multi-channel feature fusion image was used as the input of YOLO detection framework combined with image preprocessing method. Combined with SE module and ASPP module, feature weights are explored to improve feature utilization efficiency. Finally, the normalized Wasserstein distance (NWD) loss is used to replace the original IoU calculation loss to reduce the sensitivity of small target position deviation. The experimental results show that the algorithm proposed in this paper improves the accuracy by 2.5% and the recall rate by 4%. © 2022 ACM.

关键词： Deep learning

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Learning to Mine Context Information for Remote Sensing Small Object Detection

Learning to Mine Context Information for Remote Sensing Sm...

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International Conference on Guidance, Navigation and control, ICGNC 2024

作者： Li, Lang Tang, Jie Chi, Zhiqiang Niu, Yunqiang Ren, Jun Li, Lei Yao, Xiwen Cheng, Gong Han, Junwei School of Automation Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819622351

Object detection in remote sensing images has advanced significantly, nevertheless, it still faces the dilemma of low performance in detecting small objects. Due to extremely limited area, small objects can easily be submerged in complex backgrounds and suffer from insufficient context semantic information. To alleviate the aforementioned issues, we design a lightweight plug-and-play module named Cascade Local-Global Context Module (CLGCM) to extract context information. The module contains one cascade operation and two novel Sparse Context Blocks. The cascade operation can obtain long-range context semantic information at a small computational cost. And Sparse Context Block focuses on the most relevant semantic information through a sparsification operation. When integrated with Faster-RCNN and Cascade R-CNN, our module further boosts detection performance on the small object detection benchmark, AI-TOD, which significantly outperforms other mainstream algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Context information Remote sensing images Small object detection

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Transformer based Online Continuous Multi-Target Tracking with State Regression 12

Transformer based Online Continuous Multi-Target Tracking wi...

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12th IEEE International Conference on control, Automation and Information sciences, ICCAIS 2023

作者： Wei, Xinwei Chen, Linxiu Zhang, Chenyu Lin, Yiru Zhang, Linao Liu, Xiaokai Jiang, Jixiang Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China University of Electronic Science and Technology of China Research Institute of Electronic Science and Technology China

ISBN: (纸本)9798350328783

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking. © 2023 IEEE.

关键词： Forecasting

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