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Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Global context aware RCNN for object detection

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Xie, Haoyu Zhu, Mai Tie, Ming Chen, Junxin School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Science and Technology on Space Physics Laboratory Beijing100076 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Attitude control of hypersonic vehicle based on optimized ADRC 8

Attitude control of hypersonic vehicle based on optimized AD...

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8th IEEE Data Driven Control and Learning systems Conference, DDCLS 2019

作者： Piao, Minnan Sun, Mingwei Huang, Jian Wang, Zenghui Chen, Zengqiang Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China College of Artificial Intelligence Nankai University Tianjin300350 China Beijing Automatic Control and Equipment Institute Beijing100074 China University of South Africa Department of Electrical and Mining Engineering Florida1710 South Africa

ISBN: (纸本)9781728114545

In this paper, two types of practical angle of attack control schemes are proposed for hypersonic vehicle which is subject to large unmodeled, parametric uncertainties and external disturbances. In the first scheme, the pitch angle control method based on the second-order linear active disturbance rejection control is directly applied to the angle of attack control through simple command transformation. Inner-loop pitch angular rate feedback is performed first to enhance the damping in the second scheme, and then first-order linear active disturbance rejection control is designed for the overdamped plant. Both schemes provide simple and robust solutions to the angle of attack control. To achieve desired stability and performance robustness, non-smooth $H-{\infty}$ synthesis technique is utilized to optimize the controller within the designed flight envelope. Nonlinear simulations are performed to validate the advantages of the proposed methods. © 2019 IEEE.

关键词： Hypersonic vehicles

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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A summary of the development of cooperative and intelligent technology for multi-UAV systems

A summary of the development of cooperative and intelligent ...

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

作者： Zhou, Huan Su, Qian Fu, Wenxing Xu, Cheng Zheng, Meiyun Yang, Jingguang Aeronautical Engineering College Air Force Engineering University Xi'an China School of Astronautics Northwest Polytechnical University Xi'an China Unmanned System Research Institute Northwest Polytechnical University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Marine Science and Technology Northwest Polytechnical University Xi'an China Aviation Maintenance Sergeant School of Air Force Engineering University Xinyang China

ISBN: (纸本)9781728158594

In the process of formation cooperative autonomous flight and mission execution, multi-UAV systems have the characteristics of large disturbance, strong coupling of multi variables, high degree of uncertainty and complex cooperative factors, so there are many difficulties in the design of cooperative and intelligent control system. How to effectively develop the cooperative and intelligent technology of multi-UAV systems has become an urgent problem in the field of unmanned system and artificial intelligence. This paper first introduces the key technologies involved in UAV system at present, and then analyzes the challenges faced by the development of UAV system. Finally, it focuses on the development trend of multi-UAV systems cooperation and intelligent technology in the future. The research results have strong theoretical guidance significance for the cooperation and intelligent development of multi-UAV systems in China. © 2019 IEEE.

关键词： Flight control systems

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An initial alignment approach for improving the accuracy of azimuth setting

An initial alignment approach for improving the accuracy of ...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Xujun Guan Xing Wang Jianzhong Guo Wenxing Fu Lingbao Meng Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China China Aerospace Science and Industry Corporation Beijing China

ISBN: (纸本)9781728137933

Considering the azimuth discrepancy between the main and sub-inertial navigation systems (INS) for a platform-carried unmanned aerial vehicles(UAV) in case of tilted launching at a large pitch angle, an initial alignment method for improving the accuracy of the azimuth setting is proposed. First, an algorithm for computing the azimuth discrepancy is derived. Then, a mathematical model for initial alignment is established based on azimuth setting. The simulation results show that correcting the azimuth discrepancy between the main and sub-INS can greatly improve the accuracy of azimuth setting, and therefore the initial alignment accuracy of the sub-INS. Meanwhile, the drift for the gyros in three directions can be effectively estimated.

关键词： Azimuth discrepancy Azimuth setting Initial alignment Kalman filter azimuth Kalman filters MAINS alignment accuracy coplaning solidification Gyroscopes angle of pitch Mathematical Model Unmanned Aerial Vehicle drone honey bees

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Cascaded Active Disturbance Rejection Control for Near space Vehicle Autopilot

Cascaded Active Disturbance Rejection Control for Near space...

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2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

作者： Yiyin, Wei Hui, Li Zhide, Yin Yu, Fan Chuang, Song China Aerospace Science Industry Corporation Beijing100048 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781538611715

There exists a steady-state error in the closed-loop system output with some conventional linear active rejection control approaches when the pitch channel control variable of the near space vehicle is the angle of attack. In this study, a cascaded linear active disturbance rejection control approach based on the cascaded control idea is proposed. Both the inner-loop controller and the outer-loop controller are designed by the linear active disturbance rejection control method. The inner-loop is built by angular rate feedback to improve dynamic performance of the closed-loop system, and the outer-loop is built by angle of attack feedback to remove the undesired steady-state error. The approach is employed to a hypersonic near space vehicle autopilot. The numerical simulations show that the proposed approach preserves the performances on time domain and frequency domain as good as those of the conventional active disturbance rejection control approaches, and it has the better performances on disturbance rejection and angle of attack command tracking accuracy than those of some previous active disturbance rejection control approaches. © 2018 IEEE.

关键词： Angle of attack

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Joint object contour points and semantics for instance segmentation

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Zhu, Mai Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science University of Auckland New Zealand

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network’s attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8% on Cityscapes dataset and 0.8% on COCO dataset. Copyright © 2020, The Authors. All rights reserved.

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Gradient-direction-based Rectangles and Triangles Traffic Signs Detection Algorithm in Natural Scenes*

Gradient-direction-based Rectangles and Triangles Traffic Si...

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Chinese Automation Congress (CAC)

作者： YanQiong Jin XuJun Guan Hai Zhang 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 Science and Technology on Aircraft Control Laboratory Beihang University Beijing China

In this paper, we propose a novel method to detect rectangular and triangular traffic signs in natural scenes according to the geometric features of the rectangle and triangle traffic signs. It is a Gradient-direction-based method that mainly includes four stages: gradient information classification, filter templates-based line segments with different orientation extraction, structural corners detection and rectangle/triangle detection. The extracted corners are categorized into different sets according to the orientation of the line segments, by which we can quickly extract rectangle and triangle traffic signs in image. And the experimental results demonstrate under different conditions are shown that the proposed method can detect rectangular and triangular traffic signs in natural scenes efficiently and robustness.

关键词： Image segmentation Feature extraction Image edge detection Image color analysis Detection algorithms Shape Gray-scale

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