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Vision Based Target-Tracking Realized with Mobile Robots using Extended Kalman Filter

The paper presents a novel scheme for target-tracking realized with two mobile robots, where one robot is configured as tracker and the other as moving target. Fuzzy C-means clustering algorithm has been employed here to segment the target robot in images grabbed by the tracker. A new localization algorithm has also been proposed to determine the location of the target in the segmented images. An extended Kalman filter has been employed here for predicting the direction of motion of the moving target from its current and last few positions. The robot is pre-trained with back-propagation learning algorithm to plan its trajectory amidst obstacles. The pre-trained neural net is used in target-tracking application to control the motion of the tracker in the predicted direction of the moving target. Performance of the proposed neuro-Kalman synergism in target-tracking has experimentally been found to be superior to a tracking scheme without prediction by Kalman filter. The paper provides a novel approach to target-tracking realized with two mobile robots, where one robot acts as a moving target and the other plays the role of a tracker. The target robot is controlled manually, while the tracker has to predict the motion of the target and plan its trajectory to meet the target. The target-tracking problem has many interesting applications in defense, such as missile and aircraft tracking .The problem addressed in this paper, however, is restricted to 2-D only for simplicity of realization with mobile robots. The tracking problem in the present context is concerned with: i) prediction of the next location of the tracker from its previous and current positions and ii) controlling the motion of the tracker towards the predicted location of the target. Nomad Super Scout II mobile robots have been used to realize the proposed target-tracking scheme. These robots have a fixed camera mounted on top of the robot and 16 sonar transducers mounted around the cylindrical structure of the robot. To determine the current location of the moving target, the tracker rolls around its z-axis to grab an image of its target on the camera and then determine the radial distance of the target by activating the sonar sensors below the camera. The well-known extended Kalman filter algorithm has