Abstract
The growth in the sector of autonomous robots in the field of transportation has increased since the 2000s. Improving the dynamics of a robot is a continuous process to tailor its user experience. This field involves the use of planetary and extra-terrestrial robots' (called rovers) autonomous navigation capabilities. It allows analysis of terrain irregularities, climate and weather monitoring, sample collection, etc. As rovers require a significant investment, therefore, it is essential that the rover performs autonomously according to the expectations while ensuring its own safety. It is achieved by the use of complex mathematical models, image analysis techniques, machine learning (ML) and deep learning (DL) models, and allowing execution of the required tasks efficiently. Further, it provides insight on various aspects of rovers' navigation such as intended missions of rover, ML and DL models, comparison in terms of precision and accuracy, merits, and demerits along with future scope.