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检索条件"机构=Complex System Control and Intelligent Agent Cooperation Laboratory"

共 373 条记录，以下是1-10 订阅

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The Current Research Status of Unmanned Aerial Vehicle Maneuver Penetration Decision-Making 5

The Current Research Status of Unmanned Aerial Vehicle Maneu...

引用

5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zhuang, Yutao National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798331528911

In modern warfare, maneuver penetration is an essential aspect of unmanned aerial vehicle (UAV) combat operations. As the capabilities of both offensive and defensive sides continue to improve, the operational patterns of maneuver penetration scenarios are constantly evolving. This paper first introduces the maneuver capabilities and detection characteristics of both offensive and defensive systems in contemporary warfare, providing a macro definition of the maneuver penetration problem. Then, it conducts a survey and analysis of the applications of traditional and new artificial intelligence algorithms in maneuver penetration decision-making. Finally, it discusses the future research directions and challenges of artificial intelligence algorithms in this field and envisions the development of maneuver penetration decision-making. © 2024 IEEE.

关键词： Deep reinforcement learning

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Cooperative Search Strategy of Multi-UAVs Based on Hexagonal Grid 7th

Cooperative Search Strategy of Multi-UAVs Based on Hexagonal...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhao, Hongyin Hao, Mingrui Zhang, Hang Dong, Xiaofei Liu, Yuting Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819733316

Aiming at the cooperative search problem in a wide-area unknown region, a cooperative search strategy of multi-UAVs based on hexagonal grid is proposed for the fairness of decision-making, This strategy is trained by reinforcement learning method, and adopts centralized training and distributed execution, and deals with the dynamic environment problem by fusing pheromone models. Simulation shows that the cooperative search algorithm has the functions of unknown region cooperative search and online threat avoidance replanning. Compared with the square grid training method, the reinforcement learning search algorithm based on the hexagonal grid after training expands the feasible directions in each decision planning cycle, which is more suitable for low-slow-small UAVs, and the performance is more stable after convergence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Low-Light Image Enhancement Method for Consistency Judgment of Multi-view Multi-target for UAV Swarm 7th

Low-Light Image Enhancement Method for Consistency Judgment ...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Li, WeiXue Song, Chuang Hao, MingRui Zhang, Hang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819733392

To address the challenges posed by low-light images in multi-drone detection tasks, which lead to significant differences in target image features across different perspectives and poor accuracy in target association under multiple viewpoints, this paper proposes an enhanced network architecture based on MIRNet for low-light image enhancement. The goal is to improve the stability of target features in multi-view scenarios. By incorporating Sobel convolution to capture target edges and texture features, and by designing a residual contextual block combined with an attention mechanism and a pixel loss function that emphasizes the central region, the model not only acquires richer features but also focuses more on the central region's target characteristics. Experimental results demonstrate that the proposed approach effectively enhances image brightness, contrast, and target texture, exhibiting substantial advantages in both objective and subjective evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Collaborative Search Method of Heterogeneous UAVs in Gray Area 1

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Wang, Hongmin Bu, Yunong Zhao, Jiajia Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

A collaborative search planning method based on an information map is proposed for the problem of multi-moving target detection in gray areas using a heterogeneous UAV swarm. This method takes into account the detection probability and false alarm probability of sensors, the heterogeneity and flight constraints of UAVs, and the random movement of targets. The mathematical planning model for the collaborative search of multiple UAVs is built by balancing short-term gain, long-term gain, and coordination gain. An information map for search is designed, incorporating target existence probability, environmental uncertainty, and revisiting pheromones. Different planning schemes are designed based on the heterogeneous characteristics of UAVs. Through numerical simulations in typical collaborative search scenarios, the effectiveness of the proposed method is validated. The simulation results show that the proposed method can make search trajectory decisions for each UAV within seconds. The organic combination of short-term, long-term, and coordination gain can guide the UAV swarm to capture more targets. Comparative simulation results demonstrate that the proposed method can capture more targets with fewer false alarms, effectively improving the task efficiency of heterogeneous multi-UAV collaborative search. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Unmanned aerial vehicles (UAV)

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Common Aircraft Formation control Methods and Application Prospects of High Order Fully Actuated system Theory in Aircraft Formation control 7th

Common Aircraft Formation Control Methods and Application Pr...

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Liu, Xinyu Hao, Mingrui Fan, Yu Zhen, Yan Yue, Keyuan Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819733279

This article provides a review of formation control for multiple aircrafts. Firstly, several commonly used aircraft formation control methods were introduced, including leader-follower approach, behavior based formation control, virtual structure method, and artificial potential field method. Considering that the controller design of these methods is mostly based on first-order differential equations and state space methods or uses fuzzy theory or neural networks, further solutions are needed in issues such as model accuracy, controller anti-interference, and controller stability analysis. Therefore, this article proposes the possibility of applying high-order fully actuated (HOFA) system theory in aircraft formation control. In response to the nonlinear, coupled, and real-time aircraft formation control problems, the development of HOFA system theory is introduced, and it is pointed out that fully actuated system (FAS) method has outstanding advantages in dealing with nonlinear, time-varying, hysteresis problems. The article summarizes the current research and practice of FAS theory in the field of single aircraft control and multi-spacecraft formation control, and then propose a new idea of designing FAS formation control method for multiple aircrafts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aircraft

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A Combat Intention Reasoning Method for Warship Formation Targets Based on Deep Learning 1

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

With the rapid development of science and technology, modern war presents the characteristics of mathematics and information. Grasping the operational intention of battlefield targets plays a crucial role in the trend of war. The target of warship formation is an important fighting force in modern war. How to analyze and reason the target’s fighting intention accurately and timely is one of the most important parts of modern battlefield situation cognition system. Aiming at the problems of too strong subjectivity, lack of objectivity and poor model robustness existing in traditional intention reasoning methods, this paper introduces the idea of deep learning data-driven, and takes the electromagnetic, state and formation characteristic information of ship formation targets as input, designs a deep learning-based ship formation target intention reasoning model. The effectiveness of the algorithm is verified by mathematical simulation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Combat Intention Recognition of Aerial Battlefield Targets Based on LSTM 3rd

Combat Intention Recognition of Aerial Battlefield Targets B...

引用

3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819710867

In the course of military operations, the battlefield situation changes rapidly, and the combat time is fleeting. How to quickly and accurately identify the enemy’s combat intention is one of the important preconditions to ensure the commander’s correct command decision and obtain the initiative in the battlefield. In this paper, aiming at the problems of low accuracy and insufficient algorithm robustness of traditional target combat intention recognition methods in air battlefield, a combat intention recognition method based on LSTM is proposed according to the characteristics of sequence and multi-dimension of target characteristic data. Based on the historical and current state characteristics, electromagnetic characteristics and action characteristics of the target obtained by the sensor, the method uses the LSTM network to build the target combat intention recognition model, and learns the changes of the target characteristics through the network model to realize the target combat intention recognition. Simulation results show that the proposed method can effectively improve the accuracy of target combat intention recognition. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Military operations

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Cluster Collaborative Search Method Based on Dynamic Adjustment of Parameters 1

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Si, JiaShuai Hao, MingRui Liu, ZhenChang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

In order to improve the ability of unmanned aerial vehicle(UAV) cluster collaborative search and obstacle avoidance, a multi-UAV collaborative search method based on dynamic parameter adjustment is proposed. Firstly, based on the distributed model predictive control (DMPC) model, a collaborative search problem model is designed considering the rapidity of target discovery, the rapidity of covering the task area and the obstacle avoidance between drones. Secondly, the jump grid decision method not only expands the feasible solution of the search decision, but also ensures that the UAV can track the navigation point of the decision smoothly. At last, a dynamic adjustment method of search parameters is proposed, which realizes threat avoidance in collaborative search process by adjusting search parameters online. The simulation results show that the cluster cooperative search method based on dynamic parameter adjustment can fully consider the maneuverability of UAVs and realize threat avoidance during cooperative search, and improve the robustness of the algorithm after some nodes are damaged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Maneuverability

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Relative Navigation of Unmanned Aerial Vehicles Based on Data Link Ranging and Partial Guidance Information 1

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Wang, Ziwen Song, Chuang Hao, Mingrui Liang, Xinkai Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

In GNSS-denied environments, precise relative navigation between unmanned aerial vehicles (UAVs) is a key technology for achieving cooperative tasks. The relative navigation method based on data link ranging information combines altitude, airspeed measurements, and attitude information to ensure the observability of UAVs’ relative states. However, due to the significant constraints imposed by observability conditions, a novel approach is proposed that utilizes partial guidance information from the UAV swarm towards the target as additional measurements. This approach enhances the observability capability of relative navigation and alleviates the constraints imposed by the observability conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Observability

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Target Tracking Method Based on LSTM-EKF 1

引用

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Hongfeng Zhao, Jiajia Zhang, Hang Jiang, Jixiang Chen, Linxiu Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

With the development of science and technology, target tracking technology has been widely used in many fields. Through target tracking technology, the accurate estimation of target state can be obtained, and the perception ability can be effectively improved. However, due to the increasing maneuvering ability of the target, the traditional target tracking algorithm is more and more difficult to meet the requirements of maneuvering target tracking. This article in view of the traditional filtering algorithm based on bayesian theory problems of nonlinear error model, in combination with the advantages of EKF and deep learning, introducing the idea of hybrid data model, puts forward the algorithm of target tracking based on LSTM - EKF, and design a mathematical simulation experiment on the performance of the algorithm is verified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Target tracking

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