Active localization of a robot on a lattice of RFID tags by using an entropy map

We have developed a novel way for robots to estimate their pose dynamically in an environment in which RFID tags have been arranged. We previously developed a method for localizing robots using a particle filter. Testing in a room equipped with a lattice of RFID tags at 300-mm intervals revealed that the estimation fails when the robot's RFID readers are near the center of the robot's rotation because the reader could not detect enough tags by rotating movements when the robot's positions are not suitable. We have overcome this problem by developing an active localization algorithm that generates an entropy map from the RFID arrangement information, predicts the pose using a particle filter, and attracts the robot to the target using a dynamic model, the fundamental unit of which is rotation-based angular velocity. Testing demonstrated that a robot using this algorithm and an entropy map can estimate its pose robustly without falling into a dead zone by moving only about 20 cm at most.