An Inertial/RFID Based Localization Method for Autonomous Lawnmowers

Abstract Robotic lawnmowers currently available in the market cover their assigned area using a random reflection navigation strategy. While this strategy has been widely accepted for autonomous vacuum cleaning systems, it poses quality problems in outdoor applications since a randomic crossing of the garden can lead to an uneven mowing. In this paper we propose a localization algorithm based on a modified Constrained Kalman Filter that allows to implement an efficient navigation strategy and to increase the quality of service of the mower. This method properly merges data coming from an Inertial Measurement Unit (IMU) and from an RFID (Radio-Frequency IDentification) antenna with information given by the Hall sensors of the wheels of the robot. The proposed algorithm has been verified first by simulation, and then with experiments by building a prototype lawnmower robot.

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