Online Informative Motion Planning for Active Information Gathering of a Non-Stationary Gaussian Process

Abstract

Information gathering focuses on designing strategies for a robot to collect data about a physical process, aiming for accurate field reconstruction. While many recent methods have been proposed to address this problem, they often assume the model of the physical process is a priori known and stationary-assumptions that rarely hold in practice. This paper presents a novel informative motion planning approach for online information gathering of a non-stationary Gaussian process. Our approach comprises two key components: an informative path planner that explores the physical field and an adaptive velocity planner that adjusts the robot's velocity profile exploiting the field's spatial variability. Additionally, we propose a path smoothing and tracking strategy to ensure continuous robot motion. Extensive simulations on a bathymetric mapping task demonstrate the effectiveness of our approach, showing superior performance in reconstructing non-stationary physical fields compared to several baseline methods.