# A Design of Hovering System for Quadrotor UAV using Multi-Sensor Fusion

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2 In this paper, the acceleration sensor and the gyro-sensor data were converged through the complementary filter for use to measure the attitude of quadrotor, and for faster and more accurate control, the angular velocity PD controller and the angular PI controller were overlapped for use. In addition, the camera mounted in the strap-down method facing the bottom was installed to estimate the plane velocity in the inertial coordinate system, and this was con-verged with another sensor data through the complementary filter to be used in the velocity control. 2. Quadrotor Attitude Control 2.1 Quadrotor 2-axis attitude estimation When measuring the body angle with the acceleration sensor, it is vulnerable to translation and vibration, but the errors are not accumulated. And when measuring with the gyro sensor, the errors are accumulated, but it is not affected by the translation and vibration. The two sensors can measure the same physical quantities, and the properties are complementary, so the weaknesses of each sensor can be supplemented through convergence. In this paper, the method of complementary convergence on the properties of the two sensors through the complementary filter is used. (1) The formulation on the right side of the Formula (1) is the general PI control formula. is the error value to find the control value of. When the two sensor data are converged, there is no normal state error, so uses 0 or very small value, and when is too big, it can reflect even the error of, therefore, a very small value to relieve the accumulated error of is used Quadrotor 2-axis attitude control Two controllers are overlapped for use to enable 2-axis attitude control with stable and quick response. The angular PI controller inputted with the current attitude angle and target attitude angle of the quadrotor, and the angular velocity PD controller inputted with the output of angular controller and the current angular velocity of the quadrotor are used. Here, the frequency of the angular velocity controller and the angular control is 1:4. First of all, the formula of the Roll, Pitch axis acceleration controller is shown in Formula (2). f φ e φ d t dt e φ, e φ ( ) (2) The angular PI controller inputted with the Euler Angles ( ) estimated with the complementary filter and the targeted Euler Angles ( ) is shown in Formula (3). t f(t) e φ e 0 φ, e φ ( ) (3) The I controller integrates the error as time passes, and when the value inside this integrator exceeds the limit, the controller output does not react properly. This 14 Copyright c 2016 GV School Publication

3 phenomenon is called Windup, and it can be relieved by setting the upper limit on the integration amount. 3. Quadrotor velocity control The position estimation accuracy during the short period of time is not very good. Therefore, to estimate the position on the x-y plane during the short period of time indoors and outdoors, the x-y plane velocity is to be estimated in ad-vance. In this paper, the method of converging the camera mounted on the lower part of the body in the strap-down method, and the acceleration & the gyro sensor is proposed Velocity estimation using acceleration sensor The velocity can be found by removing the gravitational acceleration component in the raw data of the acceleration sensor and integrating on the time. Formula (4) is used to find the acceleration ( ) on the x-y plane. This acceleration is integrated as shown in Formula (5) to find the velocity on the x-y plane. ( p x p y p z a x )=( a y ) ( cos a z cos sin sin cos ) 9.8 (4) ( ) ( ) (5) Here, the noise of the acceleration sensor is also integrated, so estimating the precise velocity is difficult. The velocity estimation result integrating the x-axis acceleration is shown in Figure 2. The T section of this graph is the double translation section of the quadrotor across the x-axis, and the R section is the double rotational section of approximately 30 degrees centrally of the y-axis. The translation section is diverged in + direction, and the rotational section is diverged in direction, but it shows the tendency to reflect the dynamic movement appropriately. 3.2 Velocity estimation using camera In this paper, the camera mounted on the lower part of the quadrotor facing the bottom is used to estimate the movement velocity on the x-y plane of the quadrotor. In addition, the optical flow algorithm is used to estimate the pixel change of the image Optical-Flow The Spare Optical Flow is the method of reducing the computation. To use this method, you must first designate the position of the pixel to find. If the feature points such as SIFT, HOG, Haar, and Ferns are used, it may be more accurate in estimating the pixel position movement, but it causes another computation issue. Therefore, the feature point position is fixed in this paper to eliminate the time required to find the feature point. The average of the displacements ( ) measured for each frame through the optical flow is used as the displacement ( ) of the relevant image. Copyright c 2016 GV School Publication 15

4 velocity[m/s] International Journal of Smart Device and Appliance n (6) The angle of view and the resolution of the camera are fixed, and the depth between the ground and the camera can be calculated through the ultrasonic sensor, so the relative velocity ( ) on the ground can be found through Formula (7). ( 1 dt ) z tan d 2 p (7) The image displacement ( ) is differentiated with time to find the velocity, and the image velocity can be converted to the actual velocity [m/s] through the Depth information and the trigonometric function. The velocity result estimated base on this was Figure 1. Error! Reference source not found.it can be seen that movement is reflected relatively well in the translation T section without divergence, but the noise is relatively high. However, the velocity detected by the rotation of the R section is unnecessary in calculating the velocity on the x-y plane, so this is calibrated through the gyro sensor Vision Velocity Time[sec] Figure 1. Result of Camera-based speed estimation results 3.3 Velocity estimation using gyro sensor The angular velocity of the body coordinate system can be obtained through the gyro sensor. In addition, the height from the current ground can be found through the ultrasonic sensor, so the camera speed from the body rotation can be estimated through Formula (8). s sin (8) When this rotational velocity data and the camera-based velocity estimation data are added, the velocity data on the x-y plane somewhat including the noise can be obtained as shown in Figure Velocity estimation using data fusion The velocity estimated by the acceleration sensor is gradually diverged by the integration error, and the data estimated through the vision and the gyro sensor does not 16 Copyright c 2016 GV School Publication

5 Velocity[m/s] International Journal of Smart Device and Appliance diverge, but includes noise. These two features are same as the features of the gyro sensor and the acceleration sensor discussed in Chapter 2. Therefore, these two velocity information can be converted through the complementary filter as shown in Formula (9) to estimate the movement velocity ( ) in the x-y plane. x ( ) (9) Vision + Gyro Accel CF Velocity Time[sec] Figure 2. Results of speed estimation 4. Experiments The quadrotor system manufactured based on the hardware listed above is shown in Figure 3. Odroid Arduino 초음파센서 카메라 Figure 3. Appearance of experimental quadrotor To evaluate the performance on the disturbance, the rear side of the hovering quadrotor was applied with impact, and the x-axis position change was estimated. The test was conducted for approximately 1 second by pushing the side of the quadrotor for about 0.2m in constant velocity. In t applied with the first disturbance, the quadrotor was pushed approximately 0.15m, and in t applied with the second disturbance, only about 0.08m was pushed. Copyright c 2016 GV School Publication 17

6 Position[m] International Journal of Smart Device and Appliance t Time[sec] Figure 4. Result of disturbance experience with velocity controller The summary of the velocity controller test is shown in Table 1. Positional movement amount on hovering Positional movement amount on applying disturbance Before applying the velocity controller 1.3[m] 0.3[m] Table 1. Comparison of test on the velocity controller t After applying the velocity controller 0.4[m] 0.1[m] 5. Conclusions In this paper, 3-axis attitude, altitude and velocity measurement and its control method were proposed for the stabilized hovering of the quadrotor system, and it was applied to the experimental quadrotor to verify the performance. Camera, acceleration, gyro and ultrasonic sensors were used to estimate the body velocity in the inertial coordinate system. The method of finding the velocity of low noise and no divergence was proposed by converging the velocity information measured in each sensor with the complementary filter. Moreover, the velocity control was performed based on this velocity information, and when the disturbance was not applied in the x-y plane, approximately 70% of positional drift was reduced. Also, it was verified that when the controller was added, about 50% of positional movement amount was reduced on applying the disturbance, resulting in more convenient hovering. References [1] I. Sa and P. Corke, Estimation and control for an open-sourcequadcopter, in Proc. Australian Conf. Robotics and Automation (2011) [2] Jae-Nam Kim, Min-Shik Roh, Jun-Beom Song, Woo-Jin, Song, Beom-Soo Kang, and Jeong Kim, An Experimental Study of a Single Axis Seesaw Attitude Control Consisting of Motor and Propeller Journal of Advanced Navigation Technology, Vol. 16, No. 1, pp.1-7 (2012) [3] K. P. Horn and B. G. Schunk Determining optical flow Artificial Intelligence, Vol 17, pp (1980) [4] S. K. Hong, Fuzzy logic based closed-loop strapdown attitude system for unmanned aerial vehicle (UAV), In Sensors & Actuators: A. Physical 2003, Vol. 107, Issue 2, 15 October 2003, pp (2003) 18 Copyright c 2016 GV School Publication

7 [5] D. Mellinger, M. Shomin, V. Kumar, Control of quadrotors for robust perching and landing, International Powered Lift Conference 2010, pp (2010) [6] P. J. Bristeau, F. Callou, D. Vissiere, N. Prtit, The Navigation and Control technology inside the AR.Drone micro UAV, IFAC Proceedings Volumes (IFAC-PapersOnline), 18(PART 1): pp (2011) [7] K. J. Kim, D. J. Lee, Y. K. Kim, J. M. Lee (2104). Outdoor Localization for a Quad-rotor using Extended Kalman Filter and Path Planning, Journal of Institute of Control, Robotics and System, Vol. 11, Issue 03, September 2014, pp (2014) [8] S. Bouabdallah and R. Siegwart, Full Control of a quadrotor, IEEE International Conference on Intelligent Robots and Systems, : pp (2007) Copyright c 2016 GV School Publication 19

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