ItemConference Paper

Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation

Title
Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation
Creator
Kelvin Prabhu, A Anto; Vaidhehi, V.
Description
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.
Source
2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
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
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151552-2;
Format
online
Language
English
Type
Conference paper

Citation

Kelvin Prabhu, A Anto; Vaidhehi, V., “Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26217.

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