LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
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Most common techniques pertaining to the problem of mobile robot navigation are based on the use of multiple sensory data fusion and of excessive number of reference Radio Frequency (RF) stations. However, spatial layout, high navigation accuracy, and cost problems limit the applicability of those techniques in many modern robotic applications. The current manuscript outlines a novel computationally inexpensive indoor mobile robot navigation system with an intelligent processing of received signal strength (RSS) measurements of a customized Radio Frequency IDentification (RFID) system. The high navigation accuracy is achieved by incorporating a conventional stochastic Uncented Kalman Filter (UKF), which integrates RSS measurements from a customized RFID system and sensory data from the robot's wheel encoder. The workspace is defined by placing a number of RFID tags at 3-D locations. The orthogonal 2-D projection points of those tags on the ground define the target points where the robot is supposed to reach. The customized RFID system is simulated using a comprehensive electromagnetic commercial software, FEKO. The validity and suitability of the proposed navigation system are demonstrated by conducting extensive computer simulations in order to provide high-performance level in terms of accuracy and scalability.