Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kelvin Prabhu, A Anto; Vaidhehi, V. Title A name given to the resource Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SPACE65882.2025.11171365" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SPACE65882.2025.11171365</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018452941?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018452941?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kelvin Prabhu A.A., Christ University, Masters of Science (Artificial Intelligence and Machine Learning), Department of Computer Science, Bangalore, India; Vaidhehi V., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource Effective lunar navigation is difficult in rough terrain and scarce energy resources. Classical path-planning has difficulty with terrain adaptation and energy optimization. This work introduces a Reinforcement Learning (RL)-based solution for energy-optimal lunar rover navigation based on NavCam data from Chandrayaan-3. A Q-learning framework translates terrain characteristics - elevation, slope, and hazards - into a reward scheme, balancing safe travel, minimal energy consumption, and mission effectiveness. The RL agent learns to respond to varying conditions, punishing dangerous regions such as craters and slopes. Simulations on lunar grids demonstrate better energy efficiency and accuracy than traditional approaches. This research pushes autonomous planetary exploration forward, optimizing rover navigation with actual mission imagery for future lunar missions. 2025 IEEE. Subject The topic of the resource Autonomous Rover Path Planning; Chandrayaan-3; Edge Detection; Energy Cost Mapping; Feature Extraction; Homographic Grid Projection; Local Binary Patterns (LBP); Lunar Navigation; Optimal Path Planning; Q-Learning; RANSAC Holography; Reinforcement Learning; Robotic Exploration; SIFT Feature Matching; Terrain-Aware Navigation Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151552-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online