A novel method for estimating subresolution porosity from CT images and its application to homogeneity evaluation of porous media.

We propose a new method, i.e., the statistical phase fraction (SPF) method, to estimate the total porosity and spatial distribution of local porosities from subresolution pore-dominated X-ray microtomography images of porous materials. The SPF method assumes that a voxel in a CT image is composed of either a single or a maximum of three pure phases of matter (solid, liquid and air). Gaussian function (GF) fitting is conducted on the basis that the summation of the area of each GF curve is equal to the total area covered by the CT histogram. The volume fraction of each phase corresponding to each GF is calculated based on the mean value of the GF, the area of the GF, and the CT numbers for pure phases. The SPF method is verified on three different types of components containing only air and solid phases, i.e., alumina ceramic and two sintered lunar regolith simulants with relatively homogenous and inhomogeneous microstructures. The estimated porosities of a total of 15 specimens (the total porosity ranges from 0 to 51%) via the SPF method show an average error of 3.11% compared with the ground truth. Spatial distribution of local porosities in the defined representative element volume is investigated for homogeneity evaluation. Results show that the local porosity inhomogeneity in the sintered FJS-1 specimens is more prominent than that in the sintered KLS-1 specimens.

Visual SLAM-Based Robotic Mapping Method for Planetary Construction.

With the recent discovery of water-ice and lava tubes on the Moon and Mars along with the development of in-situ resource utilization (ISRU) technology, the recent planetary exploration has focused on rover (or lander)-based surface missions toward the base construction for long-term human exploration and habitation. However, a 3D terrain map, mostly based on orbiters' terrain images, has insufficient resolutions for construction purposes. In this regard, this paper introduces the visual simultaneous localization and mapping (SLAM)-based robotic mapping method employing a stereo camera system on a rover. In the method, S-PTAM is utilized as a base framework, with which the disparity map from the self-supervised deep learning is combined to enhance the mapping capabilities under homogeneous and unstructured environments of planetary terrains. The overall performance of the proposed method was evaluated in the emulated planetary terrain and validated with potential results.

Controlling soil disturbance of a lunar regolith simulant bed during depressurization in a vacuum chamber.

A dusty thermal vacuum chamber (DTVC) containing a regolith simulant bed is essential for testing equipment and techniques related to lunar surface exploration. Space agencies have been reluctant to operate a DTVC because of the challenge of controlling soil disturbance of the lunar regolith simulant bed during pumping down or depressurization, which may contaminate or even damage the chamber and vacuum equipment. There appears to be no previously available solution to this problem, or how to avoid it. We investigated the mechanism of soil disturbance during depressurization and established a criterion for evaluating its occurrence. The proposed criterion was validated by extensive experiments and numerical modelling to simulate air evacuation from soil voids. There is a critical pressure difference (CPD) between the top and bottom of the lunar regolith simulant bed that causes soil disturbance during depressurization. We found a simple equation estimating the CPD and further provided guideline on the optimum depressurization rate to avoid soil disturbance before the target vacuum level is achieved under varying soil conditions.