Automated contaminant source localization in spatio-temporal fields: A response surface and experimental design approach

We propose a contaminant detection methodology suitable for robotic automation, which is able to not only locate the source(s) of the contaminant but also estimate its intensity in an environment that is allowed to evolve over both space and time. The essential idea is to flexibly model the contaminant field surface nonlinearly via radial basis functions and to utilize basic notions from the statistical design of experiments concerning optimal placement of observations in order to make incremental decisions about robot movements. Algorithms are presented for determining such movements and the subsequent collection of measurements in three different cases corresponding to different modes of spatio-temporal evolution. The result is an iterative scheme that gradually locates the peaks (sources), as well as the entire contaminant surface. The performance of the method is assessed through simulations from known surfaces. Theoretical issues concerning convergence of parameter estimates in a multiple robots scenario are examined. The method can accommodate measurement noise and does not rely on surface gradient information.