Summary

This paper discusses the design and automated assembly of Planetary LEGO Brick for lunar in-situ construction. It describes a modular system for lunar habitat construction and uses finite element analysis and a Deep Convolutional Neural Network (DCNN) for brick recognition. No lunar regolith simulants or samples are mentioned. The paper presents a design and methodology for constructing lunar habitats using a concept called Planetary LEGO Brick. This approach involves designing modular bricks that can be assembled automatically using recognition algorithms. The study includes structural, thermal, and meteorite impact analyses to ensure the durability and safety of the habitat. Additionally, it explores automated recognition techniques, such as deep convolutional neural networks (DCNN), for efficient assembly processes in lunar environments. The paper presents the design and development of a modular, reconfigurable robotic system for lunar and planetary exploration. The system is based on LEGO Mindstorms EV3 and is designed to be scalable, adaptable, and programmable for various tasks such as terrain navigation, sample collection, and scientific experiments. The system includes a ce

Methods and applications