Modeling of observation instruments and path planning under time-variant illumination areas for water exploration in lunar polar region

Abstract

This paper proposes a novel path planning algorithm designed for efficient lunar water exploration. The proposed algorithm leverages a Bayesian network-based sensor model in combination with the RRT* algorithm. It is well-known that water is highly likely to be present in permanently shadowed regions (PSRs). However, during exploration within these regions, the rover must escape to illuminated areas within a limited time to recharge its battery. Therefore, it is crucial to develop a path planning algorithm that ensures both timely battery recharging and efficient water exploration. To address this challenge, we first introduce a sensor model based on a Bayesian network for the neutron spectrometer. Subsequently, we propose an algorithm that utilizes this sensor model and the RRT* algorithm to effectively explore water on the lunar surface. The paper provides a numerical simulation, using real data about time-variant illumination areas and time-invariant PSRs on the lunar surface, to illustrate the effectiveness of the proposed algorithm. The results reveal that the algorithm accurately and efficiently detects water under realistic illumination conditions.