GIMBALLED CAMERA CONTROL FOR ON-POINT TARGET TRACKING
Citation: Gimballed Camera Control for On-Point Target Tracking. American Research Journal of Electronics and Communication Engineering; vol 1, no. 1, pp: 1-10.
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Abstract:Detailed design of tracking system that enable a Micro Aerial Vehicle (MAV), equipped with a pan-tilt gimbaled camera to be stabilized and to track the target, with target asin center of image. The algorithm performs automatic tracking of target while camera gimbal is moved according to the target position in image.2- Axis gimbal camera set up is made with servo motors and FPV 10x zoom camera.Gimbal camera set up is integrated with APM and XBee to replicate real set up of MAV in laboratory. And it is controlled with GCS set up made of joystick, XBee, Matlab and Mission Planner software. Thus, closed loop for control of gimbal camera designed. Image processing algorithm is used to track the target and to compute the position of target in image. Accordingly, control algorithm will generate control PWM signal for control of camera gimbal.
The past decade has outstanding growth in the use of unmanned air vehicles (UAVs). Even though large UAV are capable of executing complex mission its availability is limited and costly. Thus development of low cost, small UAV (i.e Miniature Aerial Vehicle-MAV) is increased [1, 2]. MAV are used for military and civil application. One of the most significant limitations to small UAV ISR (Intelligent Surveillance and Reconnaissance) systems is their inability to carry a stabilized gimbal capable of delivering the stabilization performance required for high target resolution while the platform stays outside of its detection footprint . In order to have stable gimbal, the target should be locked to the camera. Target locking is the act of maintaining the target in the sensor’s center field of view, under target motion. In order to achieve it, target should be tracked continuously and to have the object in center of image , the camera is moved in Pan and tilt direction according to target position in image.
II. SYSTEM ARCHITECTURE
Control loop for control of gimbaled camera is shown in Fig-1.In typical operational scenario, when the MAV is in autonomous flight, the system operator may select a target of interest on the screen. Once a target is identified by the operator, the image processing tracking algorithm computes the target tracking box and provide the position of the centroids of this box in every image frame of continuous video from the camera . From the object centroid detected by the algorithm, the required pixel shift to bring the target to center of image is computed. The computed pixel difference is given as the input to an integrated MAV-gimbal control algorithm. Depending up on the pixel error, control algorithm will generate PWM signal for servos of pan ilt unit to keep the target in the center of the image frame .
Target is moved randomly with some boundary condition. (i.e. target should be always in image frame)
Initial position PWM of servo arm should be given correctly.
Fig7 shows the pixel difference dx and its corresponding PWM generated for pan servo by the algorithm.
Since it is feedback control, control action is taken after the effect. Thus, when target suddenly changes its path, error is large at that frame, then algorithm starts decreasing it
When error is large, the controller output is also large and vice-versa.When the error is around zero, PWM is constant at that point.