Intelligent Lunar Landing Site Recommender
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.
Zhang, S., Yan, Z., & Wu, F. (2013). A lunar landing safety estimation methodology using lidar acquired DEM. In: IEEE International Conference on Imaging Systems and Techniques (IST).
Ari Silburta, Mohamad Ali-Diba, Chenchong Zhub, Alan Jackson, Diana Valencia, Yevgeni Kissinb, Daniel Tamayoa, & Kristen Menoua (2018). Lunar crater identification via deep learning. Available at: https://arxiv.org/abs/1803.02192.
Hao Wang, Jie Jiang, & Guangjun Zhang. (2018). CraterIDNet: An end to end CNN for crater detection and identification in planetary images. Remote Sens., 10, 1067.
Shintaro Hashimoto & Kenji Mori. (2019). Lunar crater detection based on grid partition using deep learning. Available at: https://ieeexplore.ieee.org/document/9111474.
Nur Diyana Kamarudin, Siti Noormiza Makhtar, & Hizrin Dayana M.Hidzir (2011). Craters detection on lunar. Available at: https://ieeexplore.ieee.org/document/6015881.
Copyright (c) 2021 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.