A New Blind Selection Approach for Lunar Landing Zones Based on Engineering Constraints Using Sliding Window

Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing zones, and there are currently few effective quantitative models that can be used to select suitable landing zones. When automatic landing zones are selected, the grid method used for data partitioning tends to miss potentially suitable landing sites between grids. Therefore, this study proposes a new engineering-constrained approach for landing zone selection using LRO LOLA-based slope data as original data based on the sliding window method, which solves the spatial omission problem of the grid method. Using the threshold ratio, mean, coefficient of variation, Moran’s I, and overall rating, this method quantifies the suitability of each sliding window. The k-means clustering algorithm is adopted to determine the suitability threshold for the overall rating. The results show that 20 of 22 lunar soft landing sites are suitable for landing. Additionally, 43 of 50 landing sites preselected by the experts (suitable landing sites considering a combination of conditions) are suitable for landing, accounting for 90.9% and 86% of the total number, respectively, for a window size of 0.5° × 0.5°. Among them, there are four soft landing sites: Surveyor 3, 6, 7, and Apollo 15, which are not suitable for landing in the evaluation results of the grid method. However, they are suitable for landing in the overall evaluation results of the sliding window method, which significantly reduces the spatial omission problem of the grid method. In addition, four candidate landing regions, including Aristarchus Crater, Marius Hills, Moscoviense Basin, and Orientale Basin, were evaluated for landing suitability using the sliding window method. The suitability of the landing area within the candidate range of small window sizes was 0.90, 0.97, 0.49, and 0.55. This indicates the capacity of the method to analyze an arbitrary range during blind landing zone selection. The results can quantify the slope suitability of the landing zones from an engineering perspective and provide different landing window options. The proposed method for selecting lunar landing zones is clearly superior to the gridding method. It enhances data processing for automatic lunar landing zone selection and progresses the selection process from qualitative to quantitative.

[1]  D. Vokrouhlický,et al.  Early bombardment of the moon: Connecting the lunar crater record to the terrestrial planet formation , 2023, Icarus.

[2]  G. Wei,et al.  Illumination conditions near the Moon's south pole: Implication for a concept design of China's Chang’E−7 lunar polar exploration , 2023, Acta Astronautica.

[3]  Oladayo S. Ajani,et al.  Lunar Landing Site Selection using Machine Learning , 2023, 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS).

[4]  Jing Wang,et al.  Sliding Window Detection and Analysis Method of Night-Time Light Remote Sensing Time Series - A Case Study of the Torch Festival in Yunnan Province, China , 2022, Remote. Sens..

[5]  Xing Wang,et al.  Selection of Lunar South Pole Landing Site Based on Constructing and Analyzing Fuzzy Cognitive Maps , 2022, Remote. Sens..

[6]  Yongzhi Wang,et al.  Selection of Whole-Moon Landing Zones Based on Weights of Evidence and Fractals , 2022, Remote. Sens..

[7]  Qian Yin,et al.  Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling , 2021, Remote. Sens..

[8]  Shuyu Sun,et al.  Lunar features detection for energy discovery via deep learning , 2021 .

[9]  W. Fa,et al.  Regolith Properties in the Chang'E‐5 Landing Region of the Moon: Results From Multi‐Source Remote Sensing Observations , 2021, Journal of Geophysical Research: Planets.

[10]  B. Hu,et al.  Rock abundance and evolution of the shallow stratum on Chang'e-4 landing site unveiled by lunar penetrating radar data , 2021 .

[11]  Chunlai Li,et al.  Progress of China's Lunar Exploration (2011—2020) , 2021, Chinese Journal of Space Science.

[12]  Chunlai Li,et al.  Landing Site Selection and Overview of China’s Lunar Landing Missions , 2020, Space Science Reviews.

[13]  Miin-Shen Yang,et al.  Unsupervised K-Means Clustering Algorithm , 2020, IEEE Access.

[14]  Wei Zuo,et al.  Descent trajectory reconstruction and landing site positioning of Chang’E-4 on the lunar farside , 2019, Nature Communications.

[15]  Bülent Altunkaynak,et al.  Bootstrap confidence intervals for the coefficient of quartile variation , 2019, Commun. Stat. Simul. Comput..

[16]  Yong Wei,et al.  China’s present and future lunar exploration program , 2019, Science.

[17]  Á. Kereszturi,et al.  Terra-mare comparison of small young craters on the Moon , 2019, Icarus.

[18]  Wei Zuo,et al.  Lunar farside to be explored by Chang’e-4 , 2019, Nature Geoscience.

[19]  S. V. Gasselt,et al.  Grid Mapping the Northern Plains of Mars: Using Morphotype and Distribution of Ice‐Related Landforms to Understand Multiple Ice‐Rich Deposits in Utopia Planitia , 2019, Journal of Geophysical Research: Planets.

[20]  E. Jawin,et al.  Lunar Science for Landed Missions Workshop Findings Report , 2019, Earth and Space Science.

[21]  Jun Yan,et al.  The scientific objectives and payloads of Chang’E−4 mission , 2018, Planetary and Space Science.

[22]  Xiaohui Wang,et al.  Manned lunar landing mission scale analysis and flight scheme selection based on mission architecture matrix , 2018, Acta Astronautica.

[23]  H. Hiesinger,et al.  Geology and Scientific Significance of the Rümker Region in Northern Oceanus Procellarum: China's Chang'E‐5 Landing Region , 2018, Journal of Geophysical Research: Planets.

[24]  S. Amitabh,et al.  Potential Landing Sites for Chandrayaan-2 Lander in Southern Hemisphere of Moon , 2018 .

[25]  B. S. Daya Sagar,et al.  Categorization of hierarchically partitioned waterbody-spread via Moran's index , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[26]  M. Litvak,et al.  Selection of Luna-25 landing sites in the South Polar Region of the Moon , 2017 .

[27]  Xiaoli Sun,et al.  Summary of the results from the lunar orbiter laser altimeter after seven years in lunar orbit , 2017 .

[28]  Jun Huang,et al.  Major scientific objectives and candidate landing sites suggested for future lunar explorations , 2016 .

[29]  Carle M. Pieters,et al.  Geologic characteristics of the Luna 17/Lunokhod 1 and Chang'E-3/Yutu landing sites, Northwest Mare Imbrium of the Moon , 2015 .

[30]  Liang He,et al.  Real-time dynamic addressing for spacecraft soft landing in the lunar surface , 2015, 2015 IEEE International Conference on Information and Automation.

[31]  David E. Smith,et al.  A New Lunar Digital Elevation Model from the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera , 2015 .

[32]  Wei Zuo,et al.  The Chang’e 3 Mission Overview , 2015 .

[33]  M. Brunelli Introduction to the Analytic Hierarchy Process , 2014 .

[34]  Wei Zuo,et al.  Analysis of the geomorphology surrounding the Chang'e-3 landing site , 2014 .

[35]  Myriam Lemelin,et al.  High-priority lunar landing sites for in situ and sample return studies of polar volatiles , 2014 .

[36]  Wei Yang,et al.  Guidance navigation and control for Chang’E-3 powered descent , 2014 .

[37]  Chen Jianxin,et al.  The technical design and achievements of Chang’E-3 probe , 2014 .

[38]  S. Kaliraj,et al.  Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique , 2014, Arabian Journal of Geosciences.

[39]  C. Pieters,et al.  Atypical Regolith Processes Hold the Key to Enigmatic Lunar Swirls , 2014 .

[40]  Jianzhong Liu,et al.  The compositional distribution and rock types of the Aristarchus region on the Moon , 2013 .

[41]  D. Kring,et al.  Identification and characterization of science-rich landing sites for lunar lander missions using integrated remote sensing observations , 2012 .

[42]  Sami W. Asmar,et al.  The Crust of the Moon as Seen by GRAIL , 2012, Science.

[43]  S. V. Gasselt,et al.  Characterisation of potential landing sites for the European Space Agency's Lunar Lander project , 2012, 1208.5587.

[44]  David E. Smith,et al.  Orbit determination of the Lunar Reconnaissance Orbiter , 2012, Journal of Geodesy.

[45]  Jun Huang,et al.  Geological characteristics and model ages of Marius Hills on the Moon , 2011 .

[46]  R. Clark,et al.  Lunar mare deposits associated with the Orientale impact basin: New insights into mineralogy, history, mode of emplacement, and relation to Orientale Basin evolution from Moon Mineralogy Mapper (M3) data from Chandrayaan‐1 , 2011 .

[47]  John F. Mustard,et al.  Compositional diversity and geologic insights of the Aristarchus crater from Moon Mineralogy Mapper data , 2011 .

[48]  Masatsugu Otsuki,et al.  Japanese moon lander SELENE-2.Present status in 2009 , 2011 .

[49]  John W. Keller,et al.  Lunar Reconnaissance Orbiter (LRO): Observations for Lunar Exploration and Science , 2010 .

[50]  P. Maheshwary,et al.  Retrieving Similar Image Using Color Moment Feature Detector and K-Means Clustering of Remote Sensing Images , 2008, 2008 International Conference on Computer and Electrical Engineering.

[51]  David E. Smith,et al.  Lunar Reconnaissance Orbiter Overview: The Instrument Suite and Mission , 2007 .

[52]  Hong-Jiang Zhang,et al.  A spatial constrained K-means approach to image segmentation , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[53]  Thomas L. Saaty,et al.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[54]  Paul G. Lucey,et al.  A compositional study of the Aristarchus Region of the Moon using near‐infrared reflectance spectroscopy , 1986 .

[55]  Thomas L. Saaty,et al.  Decision making for leaders , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[56]  J. Head,et al.  Late high‐titanium basalts of the Western Maria: Geology of the Flamsteed REgion of Oceanus Procellarum , 1979 .

[57]  V. Oberbeck,et al.  Geology of the Apollo landing sites , 1969 .

[58]  P. Moran The Interpretation of Statistical Maps , 1948 .

[59]  Yaqiu Jin,et al.  Selection of a Landing Site in the Permanently Shadowed Portion of Lunar Polar Regions Using DEM and Mini-RF Data , 2022, IEEE Geoscience and Remote Sensing Letters.

[60]  I. Crawford,et al.  Regions of interest (ROI) for future exploration missions to the lunar South Pole , 2020 .

[61]  S. Deitrick,et al.  LANDING SITE ANALYSIS FOR A LUNAR POLAR WATER ICE GROUND TRUTH MISSION , 2020 .

[62]  Akos Kereszturi,et al.  Encyclopedia of Planetary Landforms , 2015 .

[63]  S. K. Abdul Rahaman,et al.  Prioritization of Sub Watershed Based on Morphometric Characteristics Using Fuzzy Analytical Hierarchy Process and Geographical Information System – A Study of Kallar Watershed, Tamil Nadu , 2015 .

[64]  David A. Kring,et al.  A global lunar landing site study to provide the scientific context for exploration of the Moon , 2012 .

[65]  V. S. Scott,et al.  The Lunar Orbiter Laser Altimeter Investigation on the Lunar Reconnaissance Orbiter Mission , 2010 .

[66]  Jiawei Han,et al.  K-Means Clustering , 2021, Learn Data Mining Through Excel.

[67]  David A. Smith,et al.  The Scientific Context for the Exploration of the Moon , 2006 .

[68]  Joy A. Crisp,et al.  Selection of the Final Four Landing Sites for the Mars Exploration Rovers , 2003 .

[69]  R. Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[70]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[71]  Thomas L. Saaty,et al.  Group Decision Making and the AHP , 1989 .

[72]  Patrick T. Harker,et al.  The Analytic Hierarchy Process , 1989 .

[73]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[74]  R. W. Saaty,et al.  The analytic hierarchy process—what it is and how it is used , 1987 .

[75]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .