Autonomous source search using Gaussian mixture model-based Infotaxis

Abstract

Searching and estimating source information such as the location and release rate, called as source term, has many applications across environmental, medical and security domains. For autonomous source search and estimation in a turbulent environment, this paper presents an information-theoretic search approach which extends the existing Infotaxis method by introducing the Gaussian mixture model (GMM). The use of the GMM for potential source locations, obtained from the particle filter, in determining candidates of the next sampling position better facilitates the balance between exploitation and exploration properties of Infotaxis, resulting in the better search and estimation performance. Numerical simulation results in various environmental setups show the superior performance of the proposed approach compared with original Infotaxis.