Online Information Gathering using Sampling-Based Planners and GPs: an Information Theoretic Approach

Abstract

Information gathering algorithms aim to intelligently select the robot actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for information gathering. However, these algorithms employ discretization of the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, most algorithms are not suited for online information gathering tasks. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: a Gaussian processes (GPs)-based prediction that allows a robot to identify highly unexplored locations, and an RRT*-based informative path planning that guides the robot towards those locations. The combination of the two steps allows an online realization of the algorithm, while eliminates the need of discretization. We demonstrate the effectiveness of the proposed algorithm in simulations, as well as with an experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles.