[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124955-en":3,"doc-seo-124955-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124955,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Small Spacecraft Design & Machine Learning-based Approaches To Lunar Robotics Navigation","The dissertation examines how future lunar exploration can be accelerated using small satellites and machine learning to handle growing mission data needs. It frames next-generation goals such as cis-lunar stations, crewed activities, and ISRU-driven missions. Off-the-shelf small-satellite components and ML-enabled automation are positioned as key enablers. Three research components are presented: dual-satellite lunar global navigation via a new triangulation theory, ML-based hopper obstacle avoidance and landing, and ML-based global path planning for small lunar rovers.","Small Spacecraft Design & Machine Learning-based Approaches To Lunar Robotics Navigation  \nby  \nToshiki Tanaka  \nA dissertation submitted to the Department of Electrical and Computer Engineering,  \nCollege of Engineering  \nin partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin Electrical and Computer Engineering  \nChair of Committee: Dr. Heidar A. Malki  \nCommittee Member: Dr. Aaron T. Becker  \nCommittee Member: Dr. Gangbing Song  \nCommittee Member: Dr. Marzia Cescon  \nCommittee Member: Dr. Steve Provence  \nUniversity of Houston  \nMay 2023  \nCopyright 2023, Toshiki Tanaka  \nABSTRACT  \nSince human exploration of the Moon in the 1960s, the lunar community has benefited from a series of successful missions, including flybys, orbiters, landers (crewed and robotic), rovers, and impactors. The next generation of lunar exploration will include a cis-lunar station, crewed missions, and in-situ resource utilization (ISRU)-based missions that will generate significant amounts of data to help answer questions about how the Moon formed and evolved, what its surface processes and resources are, and the nature of the chemical composition of its surface and deep interior.  \nComplete utilization of the currently available technologies is vital to effectively plan and execute future missions. This can be facilitated by two key technologies: small satellites and machine learning (ML) . Nowadays, satellite technologies have progressed to the point where off-the-shelf components can be purchased for small-satellite missions, greatly reducing the time and cost needed to prepare a new mission. The rapid escalation of the production and launch of small satellites has revolutionized the space industry, proving that small satellitesin constellations are more useful than fewer, larger ones for some scientific missions and radio relay missions on a large scale. ML and artificial intelligence also play an increasingly important role in aerospace applications, particularly for automated systems, including space robotics guidance, navigation, and control.  \nThis dissertation aims to demonstrate three potential components that small satellitesand ML could help accelerate in view of future exploration of the Moon and other planetary bodies. The discussion is divided into three topics: 1) renewal of lunar navigation systems with small spacecraft, 2) a machine learning-based approach to lunar hopper control, and 3) a machine learning-based approach to small rover path planning. In the first topic, a new triangulation theory that enables the creation of lunar global navigation satellite systems with just two small satellites is introduced. In the second topic, a new ML-based methodology for lunar hopper obstacle avoidance, descent, and landing is presented. In the third topic, a new ML-based global path planning methodology for small lunar rovers is proposed.  \nTABLE OF CONTENTS  \nABSTRACT iii  \nLIST OF TABLES vii  \nLIST OF FIGURES viii  \n1 Introduction 1  \n1.1 Background .................................... 1  \n1.1.1 Lunar exploration toward 2020s and beyond .............. 1  \n1.2 Motivation .................................... 4  \n1.2.1 Small spacecraft ............................. 4  \n1.2.2 Machine learning for planetary missions ................ 5  \n1.3 Focus of PhD research .............................. 7  \n1.4 Literature review ................................. 7  \n1.5 Overview ..................................... 8  \n2 Renewal of Lunar Navigation System with Small Spacecrafts 10  \n2.1 Introduction .................................... 10  \n2.1.1 Related work in depth .......................... 13  \n2.2 Method and model ................................ 18  \n2.2.1 Assumptions ............................... 18  \n2.2.2 Mathematical model ........................... 19  \n2.2.3 Systematic errors ............................. 26  \n2.3 Model validation and numerical simulation .................. 29  \n2.3.1 Simulation setup ..","cbCairWEgJPinx73","https://ap.wps.com/l/cbCairWEgJPinx73","pdf",16039282,1,172,"English","en",105,"# 1 Introduction\n## 1.1 Background\n## 1.2 Motivation\n## 1.3 Focus of PhD research\n## 1.4 Literature review\n## 1.5 Overview\n# 2 Renewal of Lunar Navigation System with Small Spacecrafts\n## 2.1 Introduction\n## 2.2 Method and model\n## 2.3 Model validation and numerical simulation\n## 2.4 Design considerations, limitations and future potentials\n## 2.5 Comparative analysis with other relating algorithms\n## 2.6 Real data demonstration\n## 2.7 Rover on-board navigation strategy\n## 2.8 Chapter summary\n# 3 Machine Learning-based Approach to Lunar Hopper Control\n## 3.1 Introduction\n## 3.2 Problem formulation\n## 3.3 Method and model","[{\"question\":\"How does the dissertation apply machine learning to lunar robotics?\",\"answer\":\"It introduces ML-based methods for lunar hopper obstacle avoidance, descent, and landing, and also proposes an ML-based global path planning approach for small lunar rovers.\"}]","Small Spacecraft Design & Machine Learning-based Approaches To Lunar Robotics Navigation | PDF",1785895590,433,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"small-spacecraft-design-machine-learning-based-approaches-to-lunar-robotics-navigation","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/small-spacecraft-design-machine-learning-based-approaches-to-lunar-robotics-navigation/124955/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does the dissertation apply machine learning to lunar robotics?","Question",{"text":75,"@type":76},"It introduces ML-based methods for lunar hopper obstacle avoidance, descent, and landing, and also proposes an ML-based global path planning approach for small lunar rovers.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]