DewTwin-Coin: an onboard autonomous framework for lunar water-ice prospecting using Chandrayaan-3 LIBS and ChaSTE data

Abstract

It is important to locate water-ice in the lunar regolith to build futuristic, sustainable bases there. But communication delays from Earth, limited bandwidth, and power constraints demand an onboard, autonomous, time-efficient decision-making framework for locating volatiles such as water-ice in the lunar regolith. In this work, we present a framework, DewTwin-Coin, that can locate potential water-ice sites in real time without drilling. Its decision-making principle is based on a find-S learning algorithm that uses surface temperature and the Hydrogen-to-Oxygen intensity ratio at a location. The core idea is a physics-based rule $-$ water-ice is stable only if the temperature is below 110 K and the Hydrogen-to-Oxygen intensity ratio is between 1.7 and 2.3. We validate DewTwin-Coin on Chandrayaan-3's 3165 laser-induced breakdown spectroscopy (LIBS) elemental data with 387 Chandra's surface thermophysical experiment (ChaSTE) data. On a 16 GB RAM system, it classified all 3165 locations within 615 seconds, yielding no strong signatures of water-ice, which matches the Chandrayaan-3 in-situ analysis. This framework is very important for future lunar and other planetary missions in which an onboard rover can autonomously determine feasible drilling locations for water-ice without issuing false drilling commands in warm terrains. Thus, the mission's onboard setup can stretch its technical limits in both power and memory capacity.