Static Attitude Determination Using Convolutional Neural Networks

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Anomaly Detection on Partial Point Clouds for the Purpose of Identifying Damage on the Exterior of Spacecrafts

The Canadarm3 is going to operate autonomously aboard the Lunar Gateway space station for the purpose of inspections and repairs. To make the repairs, damage to the spacecraft needs to be detected accurately and automatically. This research investigates methods for training Machine Learning models on 3D point clouds to identify anomalous structural damage. The PointNet algorithm was used to train models on point clouds without affecting their structure. The optimal training data style was found by comparing how well the different styles of data performed at classifying the point cloud testing data. Two different methods of anomaly detection were tested and compared; statistical anomaly detection based on classification scores and anomaly detection using an autoencoder. The autoencoder method proved superior and achieved a recall score of 90.42% with a specificity of 79.31% and a classification score of 97.93%. This showed the potential to use an autoencoder on 3D point clouds for anomalous damage detection on the exterior of spacecrafts