Strategy for on-orbit space object classification using deep learning

Abstract

Since nanosatellites are spotlighted as a verification platform for space technology, new studies on on-orbit satellite servicing using nanosatellites are being conducted. This servicing is based on space robotics using vision-based sensors in the rendezvous state with a target satellite. The space environment, such as sunlight and Earth albedo, affects the mission. Simulation of the space environment on the ground is difficult, but the development of robust algorithms which reflect the effect is essential. In particular, missions such as active debris removal require a method for overcoming changes in any known information due to external factors such as collisions. This study proposes a new strategy on nanosatellite for on-orbit space object classification by applying deep learning to sensor-based orbit satellite service activity. When previously known information is changed, a method of online learning on orbit after obtaining additional data at a short relative distance can help determine the final service part. Using the images and point cloud data that simulate the space environment, we apply a convolutional neural network and PointNet to classify the objects. The learning environment is studied using a general desktop and a micro-graphics processing unit (GPU) board that can be mounted on a nanosatellite. For the training, we used self-produced data using 3D models of nanosatellites and asteroids with similar shapes, which are difficult to distinguish with existing algorithms. Consequently, the proposed strategy by the author shows feasibility of using nanosatellite’s micro GPU for on-orbit space object classification, and it is verified that point cloud–based methods are more suitable by utilizing deep learning for nanosatellites. The proposed method with processor of nanosatellite is applicable to satellite service missions in orbit, such as capturing of robotic parts for extending life span or removing space debris.

Human limits in Machine Learning: Prediction of plant phenotypes using soil microbiome data

“…It is undeniable that machine learning (ML) has revolutionized the manner in which scientific research is performed in recent years. Among the many tasks performed by ML models in our daily lives, researchers have relied on ML to assist in clinical diagnoses [13], identification of bacterial phenotypes such as antimicrobial resistance [3], and even identification of objects in space [15]. Recently, the vast evidence of the prediction power of ML models on a wide range of applications has launched the adoption of these models in other domains such as sustainable agriculture where soil health -characterized by a wide range of biological, chemical, and physical properties [28] -is explored as an important driver to predict plant phenotypes, such as disease susceptibility or yield.…”