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Featured researches published by Haoyao Chen.


international conference on information science and technology | 2014

A 3D object recognition and pose estimation system using deep learning method

Dong Liang; Kaijian Weng; Can Wang; Guoyuan Liang; Haoyao Chen; Xinyu Wu

This paper addresses a 3D object recognition and pose estimation method with a deep learning model. We train two separated Deep Belief Networks (DBN) before connecting the last layers together to train a classifier. By this means, we can simplify the complicated 3D problem to an easier classifier training problem. The deep learning model shows its advantages in learning hierarchical features which greatly facilitate the recognition mission. We apply the new Deep Belief Networks that combine the two traditional DBNs together and assign different poses of objects as different classes in the system. Besides, to overcome the shortcoming in object detection of the deep learning model, a new object detection method based on K-means clustering is presented. We have built a database comprised of 4 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system with two cameras using the new DBNs can achieve high accuracy on 3D object recognition as well as pose estimation.


international conference on robotics and automation | 2013

Dynamics calibration of optically trapped cells with adaptive control technology

Haoyao Chen; Can Wang; Dong Sun

Optical manipulation of biological cells has recently attracted increasing attention in bioscience and nanotechnology, where optical tweezers are used as end-effectors to manipulate the cells with high precision and flexibility. Analysis of the dynamics of the optically trapped cells plays a critical role in many cell manipulation tasks such as the automatic cell transportation and force transducer. This paper presents a novel approach to calibrating the cell dynamics with the adaptive control technology. According to different measurements, two adaptive tracking controllers are designed, based on which the estimated parameters of the cell trapping dynamics (i.e., the rate of viscous coefficient and trapping stiffness) can automatically converge to the true values. Stability of the adaptive controllers and convergence of the estimated parameters are analyzed by using Lyapunov approach. Simulations and experiments of manipulating yeast cells are performed to verify the effectiveness of the proposed approach.


Sensors | 2017

A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning

Can Wang; Kang Li; Guoyuan Liang; Haoyao Chen; Sheng Huang; Xinyu Wu

The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.


international conference on computer vision systems | 2017

Visual Servo Tracking Control of Quadrotor with a Cable Suspended Load

Erping Jia; Haoyao Chen; Yanjie Li; Yunjiang Lou; Yunhui Liu

To follow a moving target, the visual servo control of a quadrotor with a cable suspended load is proposed. A monocular camera with rotation degree along y axis is equipped on the quadrotor. The dynamic model for the whole system is presented to design target tracking controller. An image based visual servoing controller is proposed to provide position and yaw reference information for the quadrotor control, when the quadrotor is too far away from target. OpenTLD is used to provide the visual feedback information for visual servoing. When the quadrotor is close enough to the target, the AprilTag technology is applied to provide the pose information for position control. Based on the dynamic model and the reference information from the visual servoing or AprilTag, a quadrotor-load PD controller is presented. Finally, simulation results are presented to illustrate the effectiveness of the proposed approaches.


IEEE Transactions on Biomedical Engineering | 2013

Flocking Multiple Microparticles With Automatically Controlled Optical Tweezers: Solutions and Experiments

Haoyao Chen; Can Wang; Yunjiang Lou


international conference on information and automation | 2013

AMCL based map fusion for multi-robot SLAM with heterogenous sensors

Baoxian Zhang; Jun Liu; Haoyao Chen


international conference on information and automation | 2015

Multi-feature based high-speed ball shape target tracking

Congyi Lyu; Yun-Hui Liu; Bing Li; Haoyao Chen


international conference on information and automation | 2015

Formation control strategy for a group of quadrotors

Shulin Liu; Can Wang; Guoyuan Liang; Haoyao Chen; Xinyu Wu


international conference on robotics and automation | 2018

Visual Grasping for a Lightweight Aerial Manipulator Based on NSGA-II and Kinematic Compensation

Linxu Fang; Haoyao Chen; Yunjiang Lou; Yanjie Li; Yun-Hui Liu


ieee international conference on real time computing and robotics | 2017

Real-time implementation of harris corner detection system based on FPGA

Shaohui Liu; Congyi Lyu; Yun-Hui Liu; Weiguo Zhou; Xin Jiang; Peng Li; Haoyao Chen; Yuanyuan Li

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Can Wang

Chinese Academy of Sciences

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Xinyu Wu

Chinese Academy of Sciences

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Guoyuan Liang

Chinese Academy of Sciences

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Yunjiang Lou

Harbin Institute of Technology

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Yun-Hui Liu

The Chinese University of Hong Kong

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Congyi Lyu

Harbin Institute of Technology Shenzhen Graduate School

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Yanjie Li

Harbin Institute of Technology

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Baoxian Zhang

Harbin Institute of Technology

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Bing Li

Harbin Institute of Technology

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Dong Liang

Chinese Academy of Sciences

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