Faculty Mentor Name
Ron Madler
Format Preference
Poster
Abstract
Orbital debris is a form of pollution that is growing at an exponential pace and puts current and future space infrastructure at risk. Satellites are critical to military, commercial, and civil operations. Unfortunately, the space they occupy is increasingly becoming more crowded and dangerous, potentially leading to a cascade event that could turn orbit around the Earth into an unusable wasteland for decades proper mitigation is not introduced. Unfortunately, existing models employed by NASA rely on a dataset created from 2D images and are missing many crucial features required for correctly modeling the space debris environment. Our approach uses highresolution 3D scanning to fully capture the geometry of a piece of debris and allow a more advanced analysis of each piece. This approach, coupled with machine learning methods, will allow advances to the current cutting edge. Physical and photographbased measurements are time-consuming, hard to replicate, and lack precision. 3D scanning allows much more advanced and accurate analysis of each debris sample, focusing on properties such as moment of inertia, cross-section, and drag. With these additional properties, we stand to substantially increase our understanding of the space debris environment through advanced characterization of each piece of debris. Once the characteristics of space debris are more thoroughly understood, we can begin mitigating the creation and danger of future space debris by implementing improved satellite construction methods and more advanced debris avoidance measures.
Included in
Space Debris Characterization Using Machine Learning Methods
Orbital debris is a form of pollution that is growing at an exponential pace and puts current and future space infrastructure at risk. Satellites are critical to military, commercial, and civil operations. Unfortunately, the space they occupy is increasingly becoming more crowded and dangerous, potentially leading to a cascade event that could turn orbit around the Earth into an unusable wasteland for decades proper mitigation is not introduced. Unfortunately, existing models employed by NASA rely on a dataset created from 2D images and are missing many crucial features required for correctly modeling the space debris environment. Our approach uses highresolution 3D scanning to fully capture the geometry of a piece of debris and allow a more advanced analysis of each piece. This approach, coupled with machine learning methods, will allow advances to the current cutting edge. Physical and photographbased measurements are time-consuming, hard to replicate, and lack precision. 3D scanning allows much more advanced and accurate analysis of each debris sample, focusing on properties such as moment of inertia, cross-section, and drag. With these additional properties, we stand to substantially increase our understanding of the space debris environment through advanced characterization of each piece of debris. Once the characteristics of space debris are more thoroughly understood, we can begin mitigating the creation and danger of future space debris by implementing improved satellite construction methods and more advanced debris avoidance measures.