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Reinforcement learning-based three-dimensional cooperative guidance law

Reinforcement learning-based three-dimensional cooperative g...

引用

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Zhen Gu Zeqi Zhang Honglong Kang Jianfeng Li Keyuan Yue Shenmin Song Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

An innovative three-dimensional cooperative guidance law for striking stationary target, based on proximal policy optimization (PPO), is introduced. This novel guidance law directly correlates engagement state information with the navigation ratio of proportional navigation guidance (PNG). Initially, the cooperative homing guidance problem is transformed into a Markov decision process, and the reward function incorporates considerations for the zero-effort-miss (ZEM) and the consensus error of time-to-go. Subsequently, the cooperative homing guidance problem is transposed into the framework of reinforcement learning (RL). Ultimately, the effectiveness of the cooperative homing guidance solution is validated through numerical simulations, encompassing agent model training and Monte Carlo cases.

关键词： Training Monte Carlo methods Navigation Markov decision processes Reinforcement learning Numerical simulation Robustness

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Relative navigation method for unmanned aerial vehicles formation using consensus unscented Kalman filter

Relative navigation method for unmanned aerial vehicles form...

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International Symposium on Autonomous systems (ISAS)

作者： Zhixing Zhuang Baichun Gong Xin Ding Mingrui Hao Linxiu Chen Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

A relative navigation method in which the measurement information between aircraft only has distance or angle was proposed to achieve accurate relative navigation between aircraft formations in the global navigation satellite system (GNSS) Denied Environment. In addition, four relative measurement schemes are analyzed. An augmented state is introduced in the relative navigation system model, and the observability analysis of the navigation system is carried out relying on the Lie derivative theory, which gives the limitation of motion: the relative position and velocity of the aircraft cannot be parallel or zero, and a single aircraft cannot keep flying straight. A consensus unscented Kalman filter (CUKF) algorithm is established using the geometric characteristics in flight. The observability of the system state is improved after using the additional constraint. Finally, a standard Monte Carlo simulation system is established, and the system simulation is carried out, which shows that the Observability analysis is verified to be correct, and the algorithm demonstrates high accuracy.

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Optimal Trajectory Generation of UAV Considering Attitude Constraints

Optimal Trajectory Generation of UAV Considering Attitude Co...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Liu, Chunjing Zhou, Hao Dong, Xiaofei Wang, Dongbin School of Astronautics Beihang University Beijing102206 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Automation Science and Electrical Engineering Beihang University Beijing100083 China

ISBN: (纸本)9789811581540

The six degrees of freedom (6-DOF) trajectory planning of quadrotors with attitude constraints is studied. Based on active disturbance rejection control (ADRC) technology, a controller for quadrotors that can track both position and attitude is designed. Firstly, the nonlinear dynamic model of quadrotors is established. Next, the optimal trajectory with attitude constraints is solved under different performance indexes. Finally, based on ADRC method, a 6-DOF tracking control algorithm for quadrotors is designed. The control effect is simulated and compared with the traditional linear control method. The results indicate that ADRC method has superior control accuracy and stability for quadrotors with large attitude maneuvers. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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A GPR-Aided Method for Collaborative Relative Navigation for Multiple Unmanned Aerial Vehicles During Ranging Measurement Outages

A GPR-Aided Method for Collaborative Relative Navigation for...

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International Symposium on Autonomous systems (ISAS)

作者： Xin Ding Mingrui Hao Baichun Gong Linxiu Chen Zhixing Zhuang Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

To maintain reliable relative navigation accuracy of unmanned aerial vehicles (UAVs) during range measurement outages, a GPR-aided ranging method is proposed to assist the extended Kalman filter for measurement updates. Specifically, when the on-board ranging measurement is available, a Gaussian process regression (GPR) algorithm is used to train the mapping model between acceleration, angular velocity of the inertial navigation system (INS), relative position, and the ranging distance. When ranging measurement is unavailable, the trained GPR algorithm is used for distance prediction, replacing the ranging measurement and being used for consensus extended Kalman filter (CEKF) measurement update to reduce the degree of relative navigation error divergence. Numerical simulation was conducted with three UAVs performing cooperative flight, the results show that the proposed algorithm can provide reliable distance prediction during ranging outage and effectively reduce the divergence of relative state estimation error.

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Genetic Algorithm for Block Relocation in Automated Warehouse 5

Genetic Algorithm for Block Relocation in Automated Warehous...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Wang, Gang Lian, Mingchang Ding, Wei Chen, Songhang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350001 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9781643684840

This paper is based on the research background of block relocation in the automated warehouse and proposes a solution utilizing AGV to relocate goods within the warehouse. Building upon the automated warehouse layout and block relocation process, we formulate a mathematical model to minimize the the number of AGV moving goods and the total travel distance of the AGV. To solve the model, we develop an enhanced genetic algorithm, which zones the block relocation process into multiple stages. Each stage integrates conventional heuristic algorithms to enhance the efficiency of the solution process. The improved genetic algorithm can effectively solve the problem, significantly enhancing the automation level of the warehouse. © 2024 The Authors.

关键词： Genetic algorithms

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WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Low-Light Enhancement 7

WATNet: A Wavelet-Aware Lightweight Hybrid Model for Fast Lo...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Wang, Qiangqiang Huang, Peiliang Li, Lei Han, Longfei Anhui University AHU-IAI Ai Joint Laboratory Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Northwestern Polytechnical University School of Automation Xi'an China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory Beijing China Beijing Technology And Business University Beijing China University of Science And Technology of China School of Information Science And Technology Hefei China

ISBN: (纸本)9798350359145

Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS. © 2023 IEEE.

关键词： Deep neural networks

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A transfer learning fusion model for semi-physical simulation and filed accuracy data 3

A transfer learning fusion model for semi-physical simulatio...

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3rd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Geng, Huapin Li, Xiaolu Li, Ziwei Suo, Bin Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing100083 China Beijing Electro-mechanical Engineering Institute Beijing100083 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783800759330

In order to solve the problem that the accuracy assessment of a single data source is subject to multiple constraints, data fusion reliability evaluation model based on transfer learning was established. Take the high-confidence but small sample field test data as the target domain, the semi-simulation test data as the source domain. Using the principal component analysis to extract the features separately, then embedding the feature distribution in glassman manifold space as two points, construct geodesic flow curve to attach them. So as to achieve data fusion, map the source and target domain to a common space through the geodesic flow curve. Accuracy evaluation can be calculated by constructing confidence region of semi-simulation data. The example verifies the feasibility and effectiveness of this model. © VDE VERLAG GMBH.

关键词： Data fusion

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Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data

Entity Recognition for Military Situation Awareness Knowledg...

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Chinese Control and Decision Conference, CCDC

作者： Linxiu Chen Weili Guan Xudong Guo Yuan Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Automation Beijing Institute of Technology Beijing China

Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM

An Approach for Upper Limb Movement Intention Recognition Us...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Weiming Zhang Xiaodong Zhang Cheng Xu Guchuan Zhou Teng Zhang Yu Wang Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Xi’an Jiaotong University Xi’an Shaanxi China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively.

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Warship Object Detection in Remote Sensing Images with Improved YOLOv5

Warship Object Detection in Remote Sensing Images with Impro...

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Chinese Automation Congress (CAC)

作者： Caiyuan Liu Yuan Li Linxiu Chen Weili Guan School of Automation Beijing Institute of Technology Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Aiming at the task of detecting ships on the sea surface in remote sensing images, there are a lot of disturbances such as variable object sizes, cloud occlusion, complex image backgrounds, and different ship orientations, etc. In this paper, we propose a ship rotating object detection network based on the improved YOLOv5, which is constructed through the strategies of introducing Swin Transformer as a feature extraction network to enhance the feature extraction performance of the network, introducing a rotating detection head to realize the detection of the rotation angle, and modifying the network loss function to accelerate the convergence of the network. The network finally achieves a 71.7% map on ShipRSImageNet validation set, which is an improvement of 2.4% compared with the original network model. The network proposed in this paper solves the problem that the YOLOv5 algorithm is unable to detect rotating objects, and the network based on the self-attention mechanism is used to further enhance the ability to detect small objects. Finally, a ship object detector that can be used in real remote sensing satellite images is obtained.

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