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IEEE International Conference on Unmanned systems (ICUS)

作者： Yinjiang Sun Rui Zhang Wenbao Liang Cheng Xu Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Regional cooperative search is an important scenario for Unmanned Aerial Vehicle (UAV) fleet. It is difficult for the UAV fleet to adapt to the dynamic environment by using traditional search method, which needs pre-planning. In this paper, a reinforcement learning approach is employed to train the UAV agents to search without pre-determined strategies. By designing reward function, state and action spaces, agents can learn to make decisions all by themselves based on their own observations and cooperate with each other effectively. The proposed method is experimentally validated by numerical simulations, and the result show that the UAV fleet can complete task with a pretty high coverage and few repetitions at the same location.

关键词： Zirconium Radio frequency Hafnium Erbium

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Zero-attracted Lorentzian Algorithm for system Identification

Zero-attracted Lorentzian Algorithm for System Identificatio...

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Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL)

作者： Yanyan Wang Qingxi Chi Xin Wang Yunong Bu Xujun Guan Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728153049

ISBN: (纸本)9781728153056

In this paper, a zero attracted Lorentzian (ZAL) algorithm is created for system identification applications. The proposed ZAL algorithm is realized by using l 1 -norm penalty on the Lorentzian-based cost function to exploit the sparseness of the existing nature signals. Moreover, a reweighting method is adopt to enhance the ability of the proposed ZAL algorithm, and the new algorithm is called as reweighting zero attracted Lorentzian (RZAL) algorithm. The proposed algorithms are presented briefly and investigated via computer simulations. The gotten results from the simulation demonstrate that the proposed algorithms are superior to the popular adaptive filtering algorithms under alpha-stable noise interference.

关键词： Signal processing algorithms Cost function Adaptive systems Mathematical model Photonics Electromagnetics Computational modeling

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Solving Ascent Trajectory Optimization Problems by Radua Pseudospectral Method

Solving Ascent Trajectory Optimization Problems by Radua Pse...

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intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Liaochao Deng Cheng Xu Weishuai You Beijing Institute of Mechanical and Electrical Engineering Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

Ascent trajectory optimization problem of air-breathing hypersonic vehicles is a highly nonlinear and nonconvex problems. Most of the early works focus on the traditional indirect method, which needs to derive the complete first-order necessary conditions of the trajectory optimization problem. The derivation process is too complicated and error-prone. Additionally, indirect method has a high demand on the initial guess, and it needs to give the initial guess of covariant variables without physical significance. In this paper, we solve the ascent trajectory optimization problem directly using Radau Pseudospectral Method (RPM). Firstly, the complex three-dimensional ascent trajectory optimization problem is established in detail. Conmmon inequality path constraints including those on dynamic pressure and aerodynamic bending moment are taken into account. The performance index is given as maximizing the final mass considering minimizing the fuel consumption. Subsequently, the ascent trajectory optimization problem is transformed into a nonlinear programming problem (NLP) by RPM. Finally, the ascent trajectory optimization for Generic Hypersonic Aerodynamic Model Example (GHAME) is solved by RPM and the optimal results demonstrate the rapidity, effectiveness and high precision of RPM. The comparison between optimal trajectories with and without path constraints shows that path constraints increase fuel consumption.

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An online state and parameter estimation of dynamic state space model of high speed train

International Journal of Control and Automation

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International Journal of control and Automation 2016年第9期9卷 51-62页

作者： Xie, Guo Zhang, Dan Hei, Xinhong Qian, Fucai Takahashi, Sei Nakamura, Hideo Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi710048 China College of Science and Technology Nihon University 7-24-1 Narashinodai Funabashi Chiba274-8501 Japan

In this paper, the state space model of high speed train is established to describe its nonlinear dynamic characteristics, whose parameters are disturbed by noise with an arbitrary distribution, and an online state and parameter identification method is proposed based on a parameters set by employing Bayesian theory and Particle Filter (PF). Firstly, the priori probabilities of all of the possible parameters are set to be equal, and the predicted states with different parameters are estimated using PF. Then the posterior probabilities of the parameters are updated by analyzing the characteristic of the measurement noise using Bayesian theory. Finally, the system sate and parameter are estimated by weighted summing all of the parameters and predicted states. The simulation results indicate that the proposed method can estimate the states and parameters of high speed train online and adaptively. © 2016 SERSC.

关键词： State space methods

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Cooperative collision avoidance method for multi-UAV based on kalman filter and model predictive control

Cooperative collision avoidance method for multi-UAV based o...

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2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Huang, Hanqiao Zhou, Huan Zheng, Meiyun Xu, Cheng Zhang, Xiaofeng Xiong, Wei Unmanned System Research Institute Northwest Polytechnical University Xi'an China Aeronautical Engineering College Air Force Engineering University Xi'an China School of Marine Science and Technology Northwest Polytechnical University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China UAV Researc Center Army Aviation Institute Beijing China

ISBN: (纸本)9781728158594

Collision avoidance is the primary problem to be solved in formation flight of multiple Unmanned aerial vehicles(UAVs). Firstly, a cooperative collision avoidance architecture of multiple UAVs is designed according to the requirement of autonomous collision avoidance of single UAV. Then a new cooperative collision avoidance method of multiple UAVs based on Kalman filter and model predictive control(MPC) is proposed. In this method, extended Kalman filter(EKF) is used to estimate the state of obstacles and target points in uncertain environment space, and to predict the trajectory of obstacles and target points. At the same time, relevant performance index functions and constraints are set up. On the basis of sharing environmental information, model predictive control strategy is used to guide and make cooperative collision avoidance decisions for multiple UAVs. The simulation results show that the proposed method is effective in uncertain environment perception and UAV collision avoidance, and the cooperative mechanism has obvious advantages. © 2019 IEEE.

关键词： Model predictive control

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A Multi-Robot Task Allocation Method Based on Graph Attention Network and Unsupervised Learning

A Multi-Robot Task Allocation Method Based on Graph Attentio...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Wu, Zirui Li, Zhen Zhu, Dong Liao, Qiuhan Yao, Weiran School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China Chengdu CHG Robots & Intelligent Equipment Institute Co. Ltd. Chengdu China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798350384185

The task allocation for multiple robots is a critical component in the coordination of unmanned *** existing heuristic algorithms are hard to achieve satisfactory results in large-scale problems, and reinforcement learning-based methods face challenges in ensuring the rationality of reward *** paper introduces a model based on multi-head attention and graph neural networks to address the schedule-dependent multi-robot task allocation problem, trained using unsupervised learning *** model can be trained with varying numbers of robots and tasks without necessitating changes to its structure or *** the experiment of this paper, the model is trained under two different conditions, and the performance is evaluated across six different problem *** the proposed model against greedy algorithms and genetic algorithms, the results demonstrate that the proposed model has significant advantages in overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Monocular Visual-Inertial Odometry Based on Point and Line Features

Monocular Visual-Inertial Odometry Based on Point and Line F...

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International Conference on Computer and Communications (ICCC)

作者： Gongyan Chen Hai Zhang Wenxing Fu Yiqun Qiao School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In order to solve the problem that the point feature tracking is not robust enough to reduce the accuracy of the system in a low-texture environment, this paper proposes a visual inertial odometry system based on point-line features (PL-VINS). Firstly, the system improves the extraction and matching algorithm of line features to increase the speed and accuracy of the algorithm; then the line features are added to the front-end tracking and back-end optimization. Finally, the EuRoc public data set is used as the experimental object to prove the effectiveness of the algorithm.

关键词： Visualization Conferences Feature extraction Cameras Real-time systems Optimization Optical flow

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Prescribed-Time Clusters Flocking for Multi-agent systems of UAVs and USVs 6

Prescribed-Time Clusters Flocking for Multi-Agent Systems of...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Shang, Hanjun Wang, Zhenhuan Tian, Yingxin Hou, Qimin Kuang, Jiyuan Gao, Yabin School of Astronautics Harbin Institute of Technology Harbin150001 China Elite Engineers School Harbin Institute of Technology Harbin150001 China The National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China School of Software Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9798350353594

This paper deals with the problem of clusters flocking for a heterogeneous multi-agent system of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs). The dynamics models of the UAVs and USVs are established separately. Due to the different state dimensions and dynamics models of the UAVs and USVs, the two types of agents are formulated with two clusters. Then, the functions of formation errors of the two clusters are constructed. For the two clusters, a new type of prescribed-time formation control laws is designed respectively. Moreover, compared with the conventional formation control that can only ensure that the multi-agent system completes ideal formations without strict time constraints theoretically, the present prescribed-time control laws can enable the multi-agent system to complete ideal formation strictly at a prescribed time independent of initial formation errors. Finally, the effectiveness of the prescribed-time formation control is verified by simulations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Topology Optimization Design of LEO Satellite Network

Topology Optimization Design of LEO Satellite Network

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第三十八届中国控制会议

作者： Jian Tai Junting Lv XiANDe Wu Ting Song Qiang Zhang Yanbing Xiang Jun Sun College of Aerospace and Civil Engineering Harbin Engineering University Shanghai Key Laboratory of Space Intelligent Control Technology Beijing Institute of Spacecraft System Engineering Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory

This paper establishes a satellite network topology optimization model based on satellite orbital dynamics, establishes constraints from two aspects of satellite geometric visibility and satellite elevation constraints in the concept of inter-satellite links, and finally solves the above optimization problems by using simulated annealing algorithm, and presents a time-varying satellite network topology optimization method. In the actual simulation, we give a satellite network with multiple satellites and optimize it. The optimal solution of the network configuration is obtained, and a shortest inter-satellite transmission path from signal start star to signal termination star based on time-varying is solved. For this inter-satellite link, the connectivity of inter-satellite link and the fault tolerance of satellite network are analyzed, which proves the feasibility and validity of the optimization method.

关键词： LEO satellite network topology optimization inter-satellite link simulated annealing algorithm

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A Hierarchical Graph Search Method for Path Planning of Unmanned Ground Vehicle for Freight Transportation

A Hierarchical Graph Search Method for Path Planning of Unma...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Hu, Jianing Li, Zhen Guo, Sen Yao, Weiran School of Astronautics Harbin Institute of Technology Harbin China Beijing Electro-Mechanical Engineering Institute Beijing China CRRC Nanjing Puzhen Co. Ltd. Nanjing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (纸本)9798350384185

For the problem of collision volume change when forklift unmanned vehicles work in complex storage environments, a multi-level graph search path planning method is proposed. The first level is the path planning of the Unmanned Ground Vehicle (UGV) body, which uses the grid map and the A* algorithm to plan the path of the UGV transferring in the scene;the second level is the joint path planning of the UGV and the object being carried, to avoid overcomplicating this issue, the unmanned vehicle will be limited to pushing the cargo only forwards. The third level is the path planning when the unmanned vehicle carries multiple objects, which decomposes the whole scene into multiple sub-scenes, plans the paths of the unmanned vehicle in the sub-scenes separately, and searches for the optimal combination of sub-scenes. Higher level searches will invoke lower level algorithms and provide a virtual scene and target as input. The experimental results show that this multilevel path planning method is optimal and complete in solving the forklift unmanned vehicle for handling a single object, and can give a feasible solution for handling multiple objects. © 2024 IEEE.

关键词： Ground vehicles

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