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Dynamic grouping of heterogeneous agents for exploration and strike missions

Frontiers of Information technology & Electronic engineering 2022年第1期23卷 86-100页

作者： Chen CHEN Xiaochen WU Jie CHEN Panos M.PARDALOS Shuxin DING School of Automation Beijing Institute of TechnologyBeijing100081China Department of Control Science and Engineering Tongji UniversityShanghai200092China Center for Applied Optimization Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleFL32611USA Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijing100081China

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat *** study aims to address the problem of dynamic construction of mission groups under new *** are heterogeneous,and a group formation method must dynamically form new groups in circumstances where missions are constantly being *** our method,a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new *** degree of matching between the mission requirements and the group’s capabilities,and the communication cost of group formation are used as indicators to evaluate the quality of the *** response threshold method and the ant colony algorithm are selected as the comparison algorithms in the *** results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

关键词： Multi-agent Dynamic missions Group formation Heuristic rule Networking overhead

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Intelligent Train Tracking control Based on Monte Carlo Tree Method

Intelligent Train Tracking Control Based on Monte Carlo Tree...

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International Conference on Intelligent Transportation

作者： Zixu Zhao Jidong Lv Boyuan Zhou Wanli Lu Ming Chai Hongjie Liu National Engineering Research Center Of Rail Transportation Operation And Control System Beijing JiaoTong University Beijing China Institute of Communication and Signalling The Center of National Railway lntelligent Transportation System Engineering and Technology Chinese Academy of Railway Science CARS Beijing China

Train tracking distance control is critical to ensure safety and enhance the transportation capacity of urban rail transit. The widely used control methods relying on the dynamics model of trains may not achieve ideal performance because of the inaccuracy of the dynamics model and delay of control commands execution, especially in the Urban Rail Transit scenarios. To overcome these limitations, this paper presents an intelligent tracking control method NN-MCTS combining a deep learning model with Monte Carlo Tree Search (MCTS) to cope with the tracking distance minimization. The deep learning model adopts a neural network (NN) to characterize the relationship between control command and actual train dynamic, while MCTS regards the control problem as a decision-making process leveraging the trajectory output of NN to obtain the reward of the corresponding control sequence. We extracted 20000 actual state trajectory segments of trains of Chengdu Metro Line 8 to train the deep-learning model, and additionally continuous trajectories as the known state of the train ahead to verify the effectiveness of the search algorithm. Experiments show the advantages of the proposed method in accuracy and stability compared with PID and real-time compared with MPC method.

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A novel 2 × 1 UWB-MIMO flower bouquet shaped antenna using CSRR reducing mutual coupling for wireless communication systems

A novel 2 × 1 UWB-MIMO flower bouquet shaped antenna using ...

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Circuit, Systems and communication (ICCSC), International Conference on

作者： Naima Dihaji Hanane Nasraoui Sudipta Das Jamal El Aoufi Mustapha Kchikach Power Electronics and System Control Lab Higher National School of Mines Rabat Laboratory of Aeronautical Telecommunication Mohammed VI International Academy of Civil Aviation Rabat Morocco Department of Electronics and Communication Engineering IMPS College of Engineering and Technology Chandipur West Bengal India Laboratory of Aeronautical Telecommunication Mohammed VI International Academy of Civil Aviation Nouaceur Morocco Power Electronics and System Control Lab Higher National School of Mines Rabat Rabat Morocco

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

In this article, a $2 \times 1$ UWB-MIMO flower bouquet has been discussed using a low-cost FR-4-epoxy material for wireless communication systems. This antenna provides a wider bandwidth impedance of $[3.42-14.64] \mathrm{GHz}$. The reflection coefficient of the proposed work achieves $\mathbf{- 3 5 . 0 4} \mathrm{dB}$. For the aims of reducing mutual coupling, a CSRR was etched on the top and the bottom of the radiation element and the isolation achieves 44.58 dB at 11.63 GHz. Furthermore, theECC parameter is less than 0.002 and the diversity gain is close to 10 dB. Moreover, the TARC parameter is low of $\mathbf{- 1 0} \mathbf{d B}$ throughout the operating bandwidth. Therefore, the proposed design is a good choice for wireless communication systems.

关键词： Wireless communication Mutual coupling Bandwidth Flowering plants Diversity methods Reflection coefficient Impedance

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Cross-Layer in MIMO-OFDM System with Adaptive Modulation and Coding: Design and Analysis

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Chinese Journal of Electronics 2025年第2期23卷 371-376页

作者： Junhui Zhao Xin Guan Xiuping Li Longji Zeng State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

The queuing performance of the cross-layer system with Space-time block code (STBC) over Multi-input-multi-output (MIMO) channel is analyzed. The Adaptive modulation and coding (AMC) in physical layer and finite length of buffer in data-link layer are combined with this model. A simple cross-layer design and the queue state process are proposed based on finite state Markov channel model, which is to solve the service process. The proposed cross-layer design is also incorporated with STBC to further improve the system performance. Simulation results illustrate the dependence of system performance on various parameters, and quantify the performance gain due to cross-layer optimization. The adjustment of target packet loss rate of adaptive modulation coding in physical layer can maximize average throughput of the system, meanwhile a significant improvement of the system performance is brought.

关键词： Cross layer design Adaptive systems System performance Simulation Packet loss Physical layer Throughput Block codes Modulation coding Optimization

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Optimization Algorithm of RGB-D SLAM Visual Odometry based on Triangulation

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International Journal of Performability engineering 2020年第3期16卷 438-445页

作者： Dong, Jingwei Jiang, Yiming Han, Zhiyu School of Measure Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

In this paper, an improved RGB-D SLAM scheme is proposed to solve the problems of great tracking error, long time-consuming in front-end, and high pressure of algorithm in back-end. The realization scheme is mainly aimed at improving the visual odometry. First, the ORB extraction algorithm is used to extract the feature points of the current frame and calculate the descriptors. Then, the current frame is matched with the local map by descriptors, and the matched feature points are added to the local map. Finally, the camera pose is calculated by the PnP algorithm. In order to ensure the tracking accuracy, a triangulation algorithm is proposed to optimize the depth value of map points in the local map. A series of fr1 datasets in the tum database are tested. The experimental results show that the real-time performance is guaranteed, and the RMSE (root mean square error) in the tracking process is reduced by 9% on average. Moreover, the image indexes of the point cloud image generated in the tracking process are also significantly improved. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Triangulation

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Quantum state and detector tomography with known rank

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IFAC-PapersOnLine 2023年第2期56卷 5881-5887页

作者： Shuixin Xiao Yuanlong Wang Jun Zhang Daoyi Dong Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Centre for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia

Quantum state tomography and quantum detector tomography are two main problems in quantum system identification. In this paper, we study state tomography and detector tomography with prior knowledge about the true rank of the unknown state/detector. In this case, we propose a sufficient condition for static (non-adaptive) tomography algorithms to achieve the optimal infidelity scaling O(1/N) on the number of state copies N. Then we present two examples of such static quantum state tomography and quantum detector tomography algorithms. Numerical examples demonstrate the effectiveness of our algorithms.

关键词： Quantum system identification quantum state tomography quantum detector tomography

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Optimal Resource Allocation for UAV-Relay-Assisted Mobile Crowdsensing

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IEEE Transactions on communications 2024年

作者： Yang, Xiaolong Fu, Yaru Zheng, Jianchao Xu, Zhan Shao, Ruihao Wu, Yuan Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beijing Information Science and Technology University School of Information and Communication Engineering Beijing100101 China Hong Kong Metropolitan University School of Science and Technology Hong Kong999077 Hong Kong Academy of Military Science Beijing100091 China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Macau China

In this paper, we exploit an emergency mobile crowdsensing (MCS) framework that utilizes unmanned aerial vehicles (UAVs) in collaboration with uncrashed base stations (BSs) to enhance sensing and communication efficiency. In the proposed framework, mobile users (MUs) equipped with sensors collect data, while UAVs, deployed as aerial relays, collaborate with uncrushed BSs to facilitate the transmission and aggregation of the sensed data from all MUs. However, the limited resources significantly affect the deployment of UAVs and the design of the UAV-relay-assisted MCS system. Moreover, selecting MUs for sensing tasks and allocating bandwidth among them are crucial factors that determine MUs' sensing capabilities and the data transmitting policies. Incorporating with foregoing essential factors, we formulate a comprehensive problem that jointly optimizes the MU selection, bandwidth allocation, UAV deployment, as well as strategies for sensing and transmitting data, aiming to improve the total reward of agent. The formulated problem poses high challenges due to the coupling between the sensing, transmission, as well as the UAVs deployment policies. To deal with this problem, we first derive the optimal transmission power and sensing data size under given MU selection, bandwidth allocation, and UAVs deployment strategy. The original optimization problem is subsequently decomposed into three folds, corresponding to finding the optimal MU selection, bandwidth allocation solution, as well as the deployment of UAVs. Meanwhile, a joint dynamic programming and a swap-then-compare enabled algorithm is proposed to obtain the optimal MU selection and bandwidth allocation policies. Next, the successive convex approximation (SCA) techniques are used to find the optimal locations for the UAVs. Extensive numerical results verify that the proposed joint algorithm can significantly outperform several benchmark approaches. © 1972-2012 IEEE.

关键词： Bandwidth

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Review of X-ray pulsar spacecraft autonomous navigation

arXiv

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arXiv 2023年

作者： Wang, Yidi Zheng, Wei Zhang, Shuangnan Ge, Minyu Li, Liansheng Jiang, Kun Chen, Xiaoqian Zhang, Xiang Zheng, Shijie Lu, Fangjun College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Beijing Institute of Control Engineering Beijing100080 China Beijing Institute of Tracking and Communication Technology Beijing100091 China Aerospace Information Research Institute Chinese Academy of Science Beijing100091 China Academy of Military Science Beijing100091 China National Innovation Institute of Defense Technology Academy of Military Science Beijing100091 China

This article provides a review on X-ray pulsar-based navigation (XNAV). The review starts with the basic concept of XNAV, and briefly introduces the past, present and future projects concerning XNAV. This paper focuses on the advances of the key techniques supporting XNAV, including the navigation pulsar database, the X-ray detection system, and the pulse time of arrival estimation. Moreover, the methods to improve the estimation performance of XNAV are reviewed. Finally, some remarks on the future development of XNAV are provided. © 2023, CC BY-NC-ND.

关键词： Navigation

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

arXiv

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting

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