Intelligent decision support for collision avoidance manoeuvre planning under uncertainty

Abstract

This paper presents a decision support system that can automatically allocate collision avoidance manoeuvres in the event of a high risk close encounter between two space objects. Decisions are supported by an Intelligent Classification System that combines Dempster-Shafer theory of evidence with Machine Learning to automatically classify conjunctions according to the probability of collision, the uncertainty on the probability of collision, the time to close approach and the cost of a collision avoidance manoeuvre. We propose a simple analytical model that allows for the fast and robust computation of both impulsive and low-thrust manoeuvres under a mix of aleatory and epistemic uncertainty. Aleatory uncertainty is the non-reducible randomness in observation data, dynamic model and parameters, while epistemic uncertainty is the lack of knowledge on system dynamics and observation data, including the model of aleatory uncertainty itself. Dempster-Shafer theory of evidence is used to model the epistemic uncertainty in the calculation of the probability of collision. Some numerical examples are included to show the performance of the collision avoidance manoeuvre optimisation strategy and of the intelligent decision support system.