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检索条件"机构=Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory"

共 454 条记录，以下是21-30 订阅

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Distributed Cooperative Search Based on DRH-NSGA Under Communication Constraint

Distributed Cooperative Search Based on DRH-NSGA Under Commu...

引用

International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Si, Jia Shuai Cheng, Jin Hao, Ming Rui Liu, Zhen Chang Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789811966125

For unmanned aerial vehicle (UAV) cooperative search mission under communication distance limitation environment, considering that the computation scale of rolling time domain optimization increases exponentially with the change of predicted steps and the problem that searching performance metrics are difficult to be selected, the distributed receding horizon non-dominated sorting genetic algorithm (DRH-NSGA) is proposed, and the distributed receding horizon (DRH) process is optimized by non-dominated sorting genetic algorithm (NSGA),and the cooperative search problem in dynamic environment is solved. Firstly, the motion and sensor models of UAV are established. Considering the possibility of interference or communication interruption during UAV communication, the communication interruption probability is added based on the communication distance constraint. In this paper, the environment awareness map is constructed, considering the motion characteristics of time-sensitive targets, and based on the updating of target probability by using Bayesian theory, the updating of time-sensitive target prediction transition probability is added to improve the accuracy of moving target existence probability map. Finally, the search performance index is designed reasonably, and the RH framework is used to optimize the search path. The simulation results show that the distributed cooperative search algorithm based on DRH-NSGA can shorten the single step planning time while ensuring the convergence speed and ensure the real-time decision-making of UAV search. At the same time, it can effectively take into account the search of moving targets after covering the search area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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A Formation control Method for Leader-Follower Aircraft Based on Particle Swarm Optimization 3rd

A Formation Control Method for Leader-Follower Aircraft Base...

引用

3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhao, Yuxia Quan, Yanli Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100074 China

ISBN: (纸本)9789819710829

As the defense system of military powers is becoming more and more complete, improving the aircraft’s penetration capabilities is particularly critical. Formation control is an effective way to improve the ability of penetration. Based on an uncontrollable situation, this paper proposes an angle-based formation control strategy for multiple aircraft. The leader’s trajectory inclination angle and trajectory deflection angle are used as the states of consistency, and the tilt angle is used as the control volume, to design the PI controller of the follower. The parameter optimization design is performed through the particle swarm optimization (PSO) algorithm. The simulation results show that the proposed angle-based formation control method can be well achieved and maintain the formation. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Aircraft

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

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

引用

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

引用

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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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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Cooperative Formation control of Fixed-Wing UAV Based on L1 Guidance Law Considering Collision Avoidance 7th

Cooperative Formation Control of Fixed-Wing UAV Based on L1 ...

引用

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

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

ISBN: (纸本)9789819733392

This article proposes a formation control scheme based on L1 guidance law for fixed wing unmanned aerial vehicle clusters while considering collision avoidance issues between them. The fixed wing aircraft cluster adopts L1 guidance and tracking method to track their respective desired waypoints. By controlling the speed of the fixed-wing UAVs, the entire cluster forms a formation configuration on their individual desired trajectories. During the formation and transformation processes, an improved artificial potential field method is proposed based on the characteristics of fixed-wing UAVs to achieve collision avoidance between clusters. The effectiveness of the proposed approach is validated through numerical simulations based on a six-degree-of-freedom nonlinear UAV model, providing a reference for practical engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fixed wings

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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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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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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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