Machine Learning for Extreme Traverse Lunar Explorer

This student team worked to apply machine learning algorithms to Lidar data and images taken during the operation of a subterranean robot in extreme environments on Earth and the Moon. The robot utilizes autonomy to operate an extreme traverse architecture (snake-like robot). The near-term application is sub-glacial environments on Earth, and real-world field data of experiments with the snake-like (EELS) robots can be provided. A desired outcome this student team worked towards was the demonstration of a machine learning algorithm to autonomously identify and potentially characterize hazards in Lidar image data taken from the field experiments of the EELS robot. These experiments would be performed in ice crevasse and other glacial-like environments. As a stretch goal, the student team could work to integrate other perception sensor data on the exterior/skin (i.e. torque sensing, temperature data) of the EELS snake robot into the ML algorithms based on Lidar imaging.

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