Inspired by the versatility of sinusoidal representation networks (SIRENs), we present a modified Guidance & Control Networks (G&CNETs) variant using periodic activation functions in the hidden layers. We demonstrate that the resulting G&CNETs train faster and achieve a lower overall… Read More »Guidance and Control Networks with Periodic Activation Functions
Identifying hazards that could compromise a safe landing is a critical step of the final phases of a lander descent trajectory. Landing on a harsh terrain can lead to the lander tipping over or prevent a deployed payload from fulfilling… Read More »AI-assisted Hazard Detection for safe lunar landing
Relative navigation solutions for planetary landing in close proximity to the surface have exploited geometry-based solutions of monocular Visual Odometry (VO) due to their robustness and accuracy. However, they encounter challenges in dynamic and low-texture environments, as well as the… Read More »Deep Visual Odometry and Pose Reconstruction through Single Image Depth Map and Triangulation
This work presents a method to compute continuous low-thrust fuel-optimal guidance updates on board a spacecraft using a combination of deep learning and differential algebraic techniques. In order to create the large datasets necessary to train neural networks, we also… Read More »Real-Time Fuel-Optimal Guidance Using Deep Neural Networks and Differential Algebra
In the field of spaceflight mechanics and astrodynamics, determining eclipse regions is a frequent and critical challenge. This determination impacts various factors, including the acceleration induced by solar radiation pressure, the spacecraft power input, and its thermal state all of… Read More »EclipseNETs: a differentiable description of irregular eclipse conditions