Mobile robot odometry relying on data fusion from RF and ultrasound measurements in a wireless sensor framework

This article focuses on the development of an integrated system for mobile robot odometry relying on an existing wireless transceiver infrastructure. The robot concurrently emits RF and ultrasound signals which are captured by the wireless sensor nodes. These nodes compute their distance from the robot and transmit back to the robot this information. The robot computes its location based on these measurements by rejecting the inaccurate ones using cluster-theory (k-means). The remaining measurements along with wheel distance (shaft encoders), orientation (magnetic compass) are used in a recursive kinematics-framework to compute in a more precise manner the robotpsilas location. Experimental studies are presented to investigate the efficiency of the localization scheme as the robot moves in curvature-controlled trajectories defined by Bezier-curves.