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Planetary Science

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Autonomous Rock Segmentation From Lidar Point Clouds Using Machine Learning Approaches, Lauren E. Flanagan Feb 2022

Autonomous Rock Segmentation From Lidar Point Clouds Using Machine Learning Approaches, Lauren E. Flanagan

Electronic Thesis and Dissertation Repository

Rover navigation on planetary surfaces currently uses a method called blind drive which requires a navigation goal as input from operators on Earth and uses camera images to autonomously detect obstacles. Images can be affected by lighting conditions, are not highly accurate from far distances, and will not work in the dark; these factors negatively impact the autonomous capabilities of rovers. By improving a rover's ability to autonomously detect obstacles, the capabilities of rovers in future missions would improve; for example, enabling exploration of permanently shadowed regions, and allowing faster driving speeds and farther travel distances. This thesis demonstrates how …


Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell Aug 2020

Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell

Electronic Thesis and Dissertation Repository

An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. …