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Found 33 papers, 11 papers with code
Closing the Gap Between SGP4 and High-Precision Propagation via Differentiable Programming
Thus, we propose a novel orbital propagation paradigm, ML-dSGP4, where neural networks are integrated into the orbital propagator.
Tracking Particles Ejected From Active Asteroid Bennu With Event-Based Vision
This paper motivates the use of a scientific event camera by reconstructing the particle ejection episodes reported by the OSIRIS-REx mission in a photorealistic scene generator and in turn, simulating event-based observations.
On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing
We anticipate that novel event-based vision datasets can be generated using this pipeline to support various spacecraft pose reconstruction problems given events as input, and we hope that the proposed methodology would attract the attention of researchers working at the intersection of neuromorphic vision and guidance navigation and control.
Investigation of the Robustness of Neural Density Fields
Hence, this work demonstrates that training neural networks for the gravity inversion problem is appropriate as long as the gravity signal is distinguishable from noise.
Optimality Principles in Spacecraft Neural Guidance and Control
Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission.
Neuromorphic Computing and Sensing in Space
The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks.
Selected Trends in Artificial Intelligence for Space Applications
The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced.
Globally Optimal Event-Based Divergence Estimation for Ventral Landing
This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing.
Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms
Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data.
The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11
Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output.
Neural representation of a time optimal, constant acceleration rendezvous
We train neural models to represent both the optimal policy (i. e. the optimal thrust direction) and the value function (i. e. the time of flight) for a time optimal, constant acceleration low-thrust rendezvous.
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap
Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions.
Geodesy of irregular small bodies via neural density fields: geodesyNets
When the body shape information is available, geodesyNets can seamlessly exploit it and be trained to represent a high-fidelity neural density field able to give insights into the internal structure of the body.
Vision-based Neural Scene Representations for Spacecraft
In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects.
Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry
To accommodate for changing light source conditions both from a directional light source (the Sun) and a diffuse light source (the sky), we extend the NeRF approach in two ways.
Spacecraft Collision Avoidance Challenge: design and results of a machine learning competition
Spacecraft collision avoidance procedures have become an essential part of satellite operations.
Safe Crossover of Neural Networks Through Neuron Alignment
Indeed, naive crossover leads to functionally damaged offspring that do not retain information from the parents.
Real-Time Optimal Guidance and Control for Interplanetary Transfers Using Deep Networks
We find that both policy learning and value function learning successfully and accurately learn the optimal thrust and that a spacecraft employing the learned thrust is able to reach the target conditions orbit spending only 2 permil more propellant than in the corresponding mathematically optimal transfer.
Satellite Pose Estimation Challenge: Dataset, Competition Design and Results
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions.
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks
ESA's PROBA-V Earth observation satellite enables us to monitor our planet at a large scale, studying the interaction between vegetation and climate and provides guidance for important decisions on our common global future.
Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
The ability to design complex neural network architectures which enable effective training by stochastic gradient descent has been the key for many achievements in the field of deep learning.
Interplanetary Transfers via Deep Representations of the Optimal Policy and/or of the Value Function
A number of applications to interplanetary trajectories have been recently proposed based on deep networks.
Learning Dynamic-Objective Policies from a Class of Optimal Trajectories
In this paper, we present a novel and straightforward approach to synthesising these policies through a combination of trajectory optimisation, homotopy continuation, and imitation learning.
Learning the optimal state-feedback via supervised imitation learning
By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback.
A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control
The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating.
On the stability analysis of deep neural network representations of an optimal state-feedback
Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks.
Machine learning and evolutionary techniques in interplanetary trajectory design
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission.
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem.
Differentiable Genetic Programming
We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming.
Real-time optimal control via Deep Neural Networks: study on landing problems
Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques.
Systems and Control
Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance
We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision.
Robotics
Designing Complex Interplanetary Trajectories for the Global Trajectory Optimization Competitions
The design of interplanetary trajectories often involves a preliminary search for options later refined/assembled into one final trajectory.
Space Physics
Revisiting Lambert's Problem
The orbital boundary value problem, also known as Lambert Problem, is revisited.
Earth and Planetary Astrophysics
