Behavior-based Mobile Robot Target Tracking Control

For mobile robot, moving targets tracking is one of the hottest researches in the world. This paper does a lot of work deeply in detail about locating and tracking moving object under the unknown environment. The system is based on behavior control and comprises three basic parts such as obstacle avoidance, target searching and target tracking.In the obstacle avoidance block, after derived the motion model of the robot and analyzed robot control technology in detail, the control strategy of mobile robot based on reinforcement learning is proposed in this paper. BP neural network is combined to Q learning algorithm. The value of reinforcement function is conformed by artificial potential field. The obstacle avoidance is realized though the above-mentioned method. Furthermore, in controlling driver motor, this paper uses PID method to control wheel speed by reading counter.This paper adopts the image preprocessing methods and target identification to meet the actual time demand In the target searching behavior. In order to rapidly searching target, the system harmonizes the robot control with the camera roam. At the same time, the camera movement is analyzed and planned in detail.In the object tracking behavior, for more precisely tracking and more smoothly control, an Extended Kalman Filter (EKF) suitable for robot system is constructed in this paper; meanwhile the relation between target positions changing in image with camera's contained angle is derived to ensure the object in the range of camera's sight.This system implements the module function under Visual C++ integrated develop environment. The experiment results prove precisely in obstacle avoidance, target identification and target tracking with real time.