Gaussian process-assisted frontier exploration and indoor radio source localization for mobile robots

Abstract: Autonomous localization of a radio source is addressed, in the context of autonomous charging for drones in indoor environments. A radio beacon will be the only input used by the robot to navigate to an unknown charging station, at an unknown area. Previous proposed algorithms used frontier-based exploration and the measured RSS to compute the direction to the source. The use of Gaussian processes is studied to model the Radio Signal Strength (RSS) distribution and generate an estimation of the gradient. This gradient was also incorporated into a frontier exploration algorithm and was compared with the proposed algorithm. It was found that the usefulness of the Gaussian process model depended on the distribution of the RSS samples. If the robot had no prior samples of the RSS, then the gradient-assisted solution performed better. Instead, if the robot had some prior knowledge of the RSS distribution, then the Gaussian process model yields a better performance.