Adaptive source localization for mobile robots based on Kriging prediction

Abstract

This paper addresses the source localization for mobile robots under the assumption of spatio-temporal invariance. Two kriging-based source localization methods, namely global traversal and adaptive gradient extremum, are proposed and implemented using limited sampling data. The global traversal method controls the robot to traverse the whole region according to a fixed trajectory, and finally fits the information distribution of the sources in the whole target region, so as to obtain the location and number of sources. The adaptive gradient extremum method initially controls the robot to collect data by traversing the target search area. Simultaneously, it uses the collected data to fit the source distribution across the entire region. Then, it utilizes gradient and extremum principles to determine the next target point iteratively, eventually reaching the positions of the sources. The simulation results show that the global traversal can obtain more field distribution information, and the adaptive gradient extremum method is more efficient.