Summary:
The goal of the paper is to enable robots to track a target in a real environment containing obstacles without a predefined map. A direction finding RFID reader uses a dual-direction antenna which determines orientation to an RF emitter by comparing the strengths of RF signals on two orthogonal spiral antennas. The strength of the RF signal is proportional to the antenna's orientation to, and the distance from, the transponder. The RFID system is based on commercial active sensor nodes produced by Ymatic Limited and attached to a Pioneer-3DX mobile robot.
A couple of different experiments were run, mapping the ratio of the strength of the RF signals to relative positions of the RF reader and transponder for different paths with different obstacles in the environment. The general trend of the signal ratio strength could be predicted using equations from classical electromagnetic theory, but the graphs were fairly noisy in practice. A different set of experiments involved having the robot with the dual sensing antennas follow a target robot which could move about. The pictures from these experiments show that the tracking works, although the path of the following robot does not adhere closely to the path of the leading robot.
Discussion:
One major advantage of the RFID technology is its lack of dependence on line of sight. This attribute is important for gesture recognition as well as robot tracking. Supposing accurate location information with sufficient granularity could be obtained from RFID sensors, I think they could be used in collecting gesture data. The small size of RFID tags make them easier to attach and more convenient to wear in a glove than ultrasonic sensors.
The fact that the directional antenna was rotated independantly from the robot suggests that the accuracy of measurements obtained from the RFID sensor would not be great enough for accurate gesture data collection. Since the magnitude of the ratio of the RFID signals varies quite a bit with environmental conditions, it would be difficult to get the kind of quality data needed for gesture recognition.
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