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Zha C.,Beihang University | Zha C.,North Institute of Computer Application | Ding X.,Beihang University | Yu Y.,Beihang University
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | Year: 2013

To meet requirement for data recording in modern micro unmanned aerial vehicle (UAV), a high-capacity data recorder based on PIC18LF4550 microcontroller and Flash memory is designed. Systematic plan and operation principle of all the modules are analyzed in detail. Four basic modules of the hardware of the data recorder are described, including the power transition module, communication interface module, flash memory module and mode-detection module. This data recorder has been tested in real time experiments. In the real time tests, the recorder can store data in a speed of 4000 Byte/s. The storage capacity of the recorder is up to 1 Gbyte while the power is low. These results show that the data recorder can fulfill the requirements of many micro UAVs for its large-capacity storage, low mass, and high speed. Source

Zha C.L.,Beihang University | Zha C.L.,North Institute of Computer Application | Ding X.L.,Beihang University | Yu Y.S.,Beihang University | Wang X.Q.,Beihang University
Applied Mechanics and Materials | Year: 2014

Micro Multi-propeller Multifunction Aerial Robot (MMAR) needs a low-cost, small-size, low-power and lightweight navigation system. To meet these requirements, a compact navigation system for micro MMARs is designed and implemented. The system consists of a GPS receiver, a MEMS inertial measurement unit (IMU), a barometer, a magnetometer and a dual microprocessors navigation processing platform. According to the characteristics of the sensors used, the optimized Kalman Filter (KF) is designed to implement the navigation data fusion with less computational cost. The test results show that the system's small size, low power consumption, light weight and better dynamic accuracy can meet the requirements of micro MMAR autonomous flight. © (2014) Trans Tech Publications, Switzerland. Source

Zha C.,Beihang University | Zha C.,North Institute of Computer Application | Ding X.,Beihang University | Yu Y.,Beihang University | Wang X.,Beihang University
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | Year: 2016

In order to meet the requirements of communication of unmanned aerial vehicle (UAV) over a long distance, the ground directional antenna automatic tracking system has been developed with the UAV location information to guide. With the real-time acquisition directional antenna vehicle position, velocity, azimuth and elevation information, the directional antenna automatic tracking ability in motion is realized and it enhances the system mobility and concealment. The traditional methods to calculate the target angle using 2 points usually need the information of relative position of the 2 points. The relative position is difficult to be obtained in real time systems. Thus a novel method is presented in this paper. The proposed method does not need to consider the relative position of 2 points, which is more convenient in applications. When the directional antenna is moving with its loading vehicle, the GPS signal is easy to be disturbed, resulting in the loss of the target tracking. The tracking system has larger motor fluctuation and greater tracking error caused by the low update frequency of the UAV position. In order to overcome these shortcomings, UAV speed is adopted to predict the position in order to realize smooth angle tracking and solve the fluctuation, thus the tracking accuracy is improved. The hardware and software design of the control system is described in detail. Test results show that the developed directional antenna has motorized tracking capability and high tracking accuracy and it can meet the requirements of UAV control. © 2016, Editorial Board of JBUAA. All right reserved. Source

Zhao X.,North Institute of Computer Application | Zhao X.,Beijing Institute of Technology | Liu P.,North Institute of Computer Application | Liu P.,Beijing Institute of Technology | And 3 more authors.
Jiqiren/Robot | Year: 2011

In order to overcome the shortcomings of traditional obstacle detection algorithms, a fast obstacle detection algorithm for mobile robots based on the vision sensor and ultrasonic sensor information fusion is proposed. The distance between the robot and the obstacle (the obstacle employed in this algorithm is rectangular) is obtained by the ultrasonic sensor located on the head of the robot. An improved fast line detection method and restriction terms are proposed to extract the straight line edge of the obstacle in the image (obtained by the vision sensor). According to the mechanism of the triangle similarity in process of imaging, the real height and width of the obstacle is calculated by fusing the distance and the height and width of the obstacle in the image, which supports the local path planning decision. The validity and practicability of the obstacle detection algorithm is tested by the experiments conducted on the hexapod robot. Source

Zhao X.,North Institute of Computer Application | Liu P.,North Institute of Computer Application | Zhang M.,North Institute of Computer Application
Proceedings 2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010 | Year: 2010

Line detection in digital images is a fundamental aspect of many problems in computer vision. In the light of the problems, such as heavy computation and intensive memory occupation, existing in the Hough Transform, an improved fast line detection algorithm combining the time-frequency domain transform and the spatial domain transform is proposed. First, the wavelet lifting is used to extract low frequency profile information while restraining high frequency noises. Second, compute the gradient of the image and threshold it to obtain a binary image. Third, based on the principles that a line can be determined by two points and a line in the image is mapped to a point in the Hough Transform, followed the detection sequence from the local to the global, map the non-zero pixels into the accumulator cells with great probability instead of all accumulator cells. Last, examine the counts of the accumulator cells to determine the parameters of the lines in the image. Experimental results demonstrate that the improved fast line detection algorithm has the performance of lower computational complexity, smaller memory occupation, and stronger robustness. © 2010 IEEE. Source

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