Publication Date

Spring 5-2024

School

School of Engineering and Computational Sciences

Major

Engineering: Electrical

Keywords

Autonomous Navigation, Mars Rover, Machine Learning, Deep Reinforcement Learning, Mobile Robots

Disciplines

Robotics

Abstract

Autonomous navigation is essential to remotely operating mobile vehicles on Mars, as communication takes up to 20 minutes to travel between the Earth and Mars. Several autonomous navigation methods have been implemented in Mars rovers and other mobile robots, such as odometry or simultaneous localization and mapping (SLAM) until the past few years when deep reinforcement learning (DRL) emerged as a viable alternative. In this thesis, a simulation model for end-to-end DRL Mars rover autonomous navigation training was created using Unity Engine, using local inputs such as GNSS, LiDAR, and gyro. This model was then trained in navigation in a flat environment using the proximal policy optimization (PPO) algorithm. The results of the training and future work are discussed.

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Robotics Commons

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