Artificial Intelligence in Space Exploration: Enhancing Autonomous Robotics for Navigation and Landing in Extreme Space Environments

Artificial Intelligence in Space Exploration: Enhancing Autonomous Robotics for Navigation and Landing in Extreme Space Environments | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 June 2025 V1 Latest version Share on Artificial Intelligence in Space Exploration: Enhancing Autonomous Robotics for Navigation and Landing in Extreme Space Environments Authors : Ahmad Alhosban 0000-0001-7494-6067 [email protected] , Mohamad Abu Amsha , Abdalrahman Ghazal , Nawras Bin Tareef , and Ahmad Yu Authors Info & Affiliations https://doi.org/10.22541/au.174947260.06507415/v1 261 views 128 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Autonomous planetary landing is one of the toughest challenges in space exploration, especially in unfamiliar and rough terrains where quick, smart decisions are crucial. This research proposes an integrated system that uses Artificial Intelligence AI, terrain sensing, and vision analysis to improve landing navigation accuracy and safely performance. It combines LIDAR-based elevation maps and CCD images, then it is been analyzed by Convolutional Neural Networks (CNNs) to identify hazards and choose safe landing spots. A landing guidance system using an Extended Kalman Filter (EKF) and PID control adjust the landing path in real time and saves fuel. As a result, simulations show a significant improvement in landing error, it dropped from 150m in previous studies to nearly 20m. In addition, fuel efficiency rose from 70% to 85%, and hazard avoidance improved from 60% to 92%. These results show the ability of the proposed system to make smart decisions on its own, reduce the need for human control, and handle complex surfaces. The framework’s flexible design, real-time adaptability and trajectory stability make it a strong candidate for future missions to Mars, asteroids, and beyond. Supplementary Material File (artificial intelligence in space exploration enhancing autonomous robotics for navigation and landing in extreme space environments v3.docx) Download 562.26 KB Information & Authors Information Version history V1 Version 1 09 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence autonomous navigation space robotics Authors Affiliations Ahmad Alhosban 0000-0001-7494-6067 [email protected] Amman Arab University View all articles by this author Mohamad Abu Amsha Amman Arab University View all articles by this author Abdalrahman Ghazal Tafila Technical University View all articles by this author Nawras Bin Tareef Amman Arab University View all articles by this author Ahmad Yu Amman Arab University View all articles by this author Metrics & Citations Metrics Article Usage 261 views 128 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ahmad Alhosban, Mohamad Abu Amsha, Abdalrahman Ghazal, et al. Artificial Intelligence in Space Exploration: Enhancing Autonomous Robotics for Navigation and Landing in Extreme Space Environments. Authorea . 09 June 2025. 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