In this work, a dynamic model is proposed to simulate the drilling and steering process of an autonomous burrowing mole to access scientific samples from the deep subsurface of the Moon. The locomotive module is idealized as a rigid rod. The characteristic parameters are considered, including the length, cross-section diameter, and centroid of a cylindrical rod. Based on classical Lagrangian mechanics, a 3-DOF dynamic model for the locomotion of this autonomous device is developed. By introducing resistive force theory, the interaction scheme between the locomotive body and the lunar regolith is described. The effects of characteristic parameters on resistive forces and torques are studied and discussed. Proportional-derivative control strategies are introduced to calculate the tracking control forces following a planned trajectory. The simulation results show that this method provides a reliable manipulation of a mole-type robot to avoid obstacles during the tracking control process in layered sediments. Overall, the proposed reduced-order model is able to simulate the operating and controlling scenarios of an autonomous burrowing robot in lunar subsurface environments. This model provides intuitive inputs to plan the space missions of a drilling robot to extract subsurface samples on an extraterrestrial planet such as the Moon or Mars.

Drilling parameters are intricately linked to the mechanical interactions between the drilling device and lunar regolith, significantly affecting sampling characteristics. Achieving high coring efficiency requires a deep understanding of how these parameters influence the coring rate. While experiments can characterize coring performance, they are time-consuming and costly due to the need for numerous repetitive tests. To address this, this paper integrates experiments with machine learning to develop a surrogate model that replaces experimental results, allowing for extrapolation and interpolation to analyze the sensitivities of drilling parameters. Specifically, Taguchi orthogonal experiments are designed to gather a representative sample set, and a Bayesian neural network (BNN) is employed to create a predictive model which can quantify uncertainty. Active learning is then applied to focus additional sampling on uncertain regions, refining the model iteratively to achieve an accurate approximation of experimental results. This approach reduces the number of experiments needed to develop a high-fidelity surrogate model. Validation results demonstrate strong extrapolation accuracy and generalization, with an R² of 0.89 and a MAPE of 5.74% on the independent test set. Based on this model, this paper analyzes the influence of drilling parameters on sampling performance, providing insights for optimizing drilling parameters to maximize coring efficiency.

Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R² of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration.

Returning extraterrestrial samples to Earth for analysis is crucial for planetary geological research and resource utilization. However, during their return journey, these samples undergo significant environmental changes, such as varying pressure, temperature, impact, and vibration. Hence, a dependable sealing technique that preserves sample integrity without requiring high-powered tools is necessary. This article aims to develop an automatic sealing system for collecting extraterrestrial samples, conducting parametric design and mechanical analysis on two types of proposed sealing structures with minimal sealing force as the benchmark. Additionally, the system will undergo validation through sealing and leakage detection experiments. An automated sealing system, capable of storing samples in multiple sampling tubes, is assembled for the extraterrestrial sampling device.

Interplanetary Dust Particles (IDPs) contain rich information about the solar system, which attract a high scientific attention in a sample return mission. This type of spacecraft in the low earth orbit may encounter and collect IDPs through a passive receptor. It is a dynamic impact process, since IDPs travel at a hypervelocity around 7\~8 km/s or even higher. SiO~2~ aerogel has