Utilisation of Moon Regolith for Radiation Protection and Thermal Insulation in Permanent Lunar Habitats

In the context of a sustainable long-term human presence on the Moon, solutions for habitat radiation and thermal protection with regolith are investigated. Regolith compression is studied to choose the optimal density-thickness combination in terms of radiation shielding and thermal insulation. The applied strategy is to protect the whole habitat from the hazards of galactic cosmic rays and design a dedicated shelter area for protection during solar particle events, which eventually may be a lava tube. Simulations using NASA’s OLTARIS tool show that the effective dose equivalent decreases significantly when a multilayer structure mainly constituted of regolith and other available materials is used instead of pure regolith. The computerised anatomical female model is considered here because future missions will be mixed crews, and, generally, more sex-specific data are required in the field of radiation protection and human spaceflight. This study shows that if reasonably achievable radioprotection conditions are met, mixed crews can stay safely on the lunar surface. Compressed regolith demonstrates a significant efficiency in thermal insulation, requiring little energy management to keep a comfortable temperature inside the habitat. For a more complete picture of the outpost, the radiation protection of lunar rovers and extravehicular mobility units is considered.

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

A Proposal for Long-Term Missions of Small Lunar Rovers Using an MLI Curtain System

This paper proposes a lunar night survival method for small rovers using an MLI (Multilayer Insulation) curtain system for long-term missions. Until recently, it was difficult to install RHU (Radioisotope Heating Units) or other temperature maintenance devices on small lunar rovers to enable lunar night survival, and so such rovers could only perform short two-week missions. Thermal analysis results show that small rovers could survive during lunar nights by moving into a shelter located inside the MLI curtain of the lander without mounting temperature maintenance devices. In order to enhance the feasibility of the MLI curtain system, we also propose ideas of a double-layer MLI and a rover configuration without solar cells.