High Precision Position Estimation of a Centimeter Scaled Robot using Deep Learning and an Actively Controlled Magnetic Field

This paper develops a 2D position estimation system for a robot moving over a plane using an actively controlled magnetic field. The position estimation system consists of two magnetic sensors on the moving robot and an actively controlled external rotating permanent magnet. The orientation of the external magnet is controlled to roughly point at the robot and a narrow localized magnetic field map is developed using deep learning around the pointing direction of the magnet. Using the magnetic fields at both sensors, the radial and polar positions of the robot are estimated using an unscented Kalman filter. The orientation of the magnet is then more finely controlled to point precisely at one of the magnetic sensors. This enables the design of an asymptotically stable nonlinear observer that provides enhanced accuracy in the radial position estimation of the robot. Experimental results are presented on the performance of the estimation system for a robot moving in a 2D plane.