Intelligent Lunar Landing Site Recommender

Authors

  • Anthony Thomas Student, Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, INDIA
  • Digina Derose Student, Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, INDIA
  • Sahaya Cyril Student, Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, INDIA
  • Smita Dange Assistant Professor, Department of Computer Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, INDIA

DOI:

https://doi.org/10.31033/ijemr.11.2.26

Keywords:

Machine Learning, Lunar Terrain, Craters, Soft Landing Sites

Abstract

Space exploration is brewing to be one of the most sought after fields in today’s world with each country pooling in resources and skilled minds to be one step ahead of the other. The core aspect of space exploration is exoplanet exploration, i.e., by sending unmanned rovers or manned spaceships to planets and celestial bodies within and beyond our solar system to determine habitable planets. Landscape inspection and traversal is the core feature of any planetary exploration mission. It is often a strenuous task to carry out a machine learning experiment on an extraterrestrial surface like the Moon. Consequent lunar explorations undertaken by various space agencies in the last four decades have helped to analyze the nature of the Lunar Terrain through satellite images. The motion of the rovers has traditionally been governed by the use of sensors that achieve obstacle avoidance. In this project we aim to detect craters on the lunar landscape which in turn will be used to determine soft landing sites on the lunar landscape for exploring the terrain, based on the classified lunar landscape images.

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Published

2021-04-30

How to Cite

Anthony Thomas, Digina Derose, Sahaya Cyril, & Smita Dange. (2021). Intelligent Lunar Landing Site Recommender. International Journal of Engineering and Management Research, 11(2), 184–188. https://doi.org/10.31033/ijemr.11.2.26