This study investigates indoor localization problem of robot or a customer at shopping mall environment. To improve the localization accuracy, a sensor fusion-based approach is employed, which combines data from ZigBee, odometry of active shopping cart (ASC), and QR marker. The proposed algorithm employs Gaussian probability estimation method and thus it is adaptive to localization problem even at noisy environment such as the shopping mall. To implement the localization service, an ASC which is equipped with motors for navigation, a laser sensor for tracking, and a tablet computer for human-computer interaction is designed. Through experimental work, we corroborate the feasibility of the proposed localization algorithms.
This paper proposes an automatic lighting system using multiple 3-DOF robotic lamps. The robotic lamp is a spherical type parallel mechanism with a unique forward kinematic solution. In order to minimize the dynamic load, three motors are installed at the base frame and a counter balancing design is performed. Using the coordinate transformation between the environment coordinate and the robot coordinate, the position data of the human is converted to three output angles of the robotic lamp. Positions of people are measured by a laser scanner, and in order to track the positions of people accurately by multiple robotic lamps, the Kalman filter algorithm and a data association algorithm were applied. In experiment, three robotic lamps were employed to track three people independently. For each lamp, two tilting angles are controlled for a localized light and the zoom mechanism is employed to control the intensity of the light.
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