Towards intelligent trajectory optimisation in astrodynamics

Abstract

In this work trajectory optimisation is explored through four different avenues, namely: 1. Direct Methods, 2. Heuristics, 3. Reinforcement learning, 4. Supervised Learning Each method builds off of its preceding session. It is shown that each method poses the capability to be utilised for autonomous real-time optimal control in astrodynamics applications.

A housefly is a rather simple organism, yet it is able to independently make decisions to achieve its goals, such as navigating to a food-source and avoiding obstacles. Inspecting closer, a housefly is able to make these decisions instantaneously, such as in the case of being swatted at by a human. If one thinks about the descent of a landing capsule onto the Martian surface, the nature of the situation is quite the same. Because communication with Earth is prolonged, the lander must make decisions on its own in order to safely land on the surface. If a common housefly can independently make decisions in real-time, in uncertain dynamic environments, than surely a spacecraft should be able to do the same in an environment where the objective is clearly outlined.