Intelligent Navigation System For Planetary Rovers Using AI

Abstract

Planetary exploration missions demand highly autonomous and intelligent robotic systems capable of navigating unstructured, dynamic, and communication-constrained extraterrestrial terrains. Traditional rule-based navigation approaches often fall short when dealing with the uncertainty, latency, and variability inherent to planetary environments such as Mars or the Moon. This thesis presents a comprehensive framework for an intelligent navigation system designed specifically for planetary rovers, integrating advanced artificial intelligence (AI) methodologies, including deep reinforcement learning, vision-based terrain analysis, and adaptive planning mechanisms.
The core contribution of this research lies in the design and implementation of a modular, end-to-end learning system utilizing Proximal Policy Optimization (PPO) algorithms, tailored for partial observability and sparse reward conditions. A state-action mapping architecture is proposed to enable context-aware motion decisions from raw sensory inputs, minimizing reliance on pre-defined heuristics. Extensive simulations were conducted using the Gazebo robotic environment to evaluate the effectiveness of the system across various Martian-like terrains.
In addition, a comparative evaluation against traditional planning methods was performed to assess improvements in obstacle avoidance, route efficiency, and adaptability under sensor noise. Ethical implications of autonomous decision-making in high-stakes planetary missions were also examined through a layered AI-Human override model. A specialized SPACE-AI-Ethics framework is introduced to address transparency, accountability, and societal trust in autonomous space robotics.
The findings of this thesis contribute to the evolving landscape of intelligent robotic autonomy in space exploration. It offers scalable solutions to real-world challenges including delayed Earth-to-rover communication, limited computational resources onboard, and the critical need for reliable AI decision-making in unstructured environments. Future work explores bio-inspired algorithms, swarm-based rover cooperation, and the inclusion of thermal and geological data fusion to enhance mission success in extreme planetary conditions.