Haoyao Chen

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

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.

Dynamics calibration of optically trapped cells with adaptive control technology

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.

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

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.

Visual Servo Tracking Control of Quadrotor with a Cable Suspended Load

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.