Overview

Astro.IQ is an innovative framework designed to elevate spacecraft trajectory optimization through the power of machine learning. Taking inspiration from the nimble decision-making abilities of a housefly, this library empowers spacecraft to navigate complex environments and make autonomous landing decisions on planets like Mars. Given the communication delays with Earth, having an intelligent system that can react swiftly is vital, and Astro.IQ looks to bridge this gap through advanced computational techniques.

With a focus on astrodynamics applications, Astro.IQ integrates various machine learning techniques to tackle common challenges in space navigation. Whether it's planning efficient cislunar trajectories, executing planetary landings, or optimizing interplanetary paths, this library provides the tools necessary for modern space exploration.

Features

Machine Learning Integration: Implements advanced AI techniques to enhance decision-making and adaptability in dynamic space environments.
Versatile Trajectory Optimization: Supports multiple trajectory types, including planetary landings and cislunar trajectories, ensuring mission versatility.
Robust Computational Methods: Utilizes various trajectory transcription methods such as Euler, Trapezoidal, and Runge-Kutta for accurate trajectory simulation.
Database Generation: Incorporates homotopic trajectory transitioning for robust database creation, aiding decision-making processes.
Neural Network Architectures: Provides options for shallow and deep feed-forward networks, as well as recurrent networks to accommodate different learning needs.
Cutting-Edge Techniques: Features state-of-the-art methodologies like Deep-Q Learning and Guided Policy Search for enhanced trajectory optimization.
Application Ready: Designed with practical applications in mind, making it ideal for researchers and engineers in aerospace and astrodynamics.

Cisprague

Astro.IQ