Autonomous Trajectory Optimisation for Intelligent Satellite Systems and Space Traffic Management

Recent advances in Artificial Intelligence (AI), sensing and computing technologies have led to the development of new promising concepts for the safe and efficient operation of Distributed Space Systems (DSS) in near-Earth orbits. The combined use of these technologies allows higher levels of autonomy in small satellite constellations or clusters, facilitating a more responsive and resilient approach to Space-Based Space Surveillance (SBSS), both in terms of data collection and data processing. This article presents an innovative DSS mission management approach exploiting multiple heterogeneous space platforms capable of autonomously calculating attitude and orbit raising manoeuvres to maximise mission efficiency and minimise the risk of collision with resident space objects. Furthermore, it addresses the development of reactive mission planning capabilities and lays foundations for introducing predictive system functionalities, by adopting heuristic multiple-direct-shooting trajectory optimisation algorithms based on the particle swarm technique. These functionalities provide DSS with higher levels of autonomy and support the introduction of new mission concepts while mitigating the threats of the space environment. The first verification case study addresses the problem of an orbit raising manoeuvre for debris collision avoidance, where the thrust activation and vectoring angle profiles are optimised to achieve the orbital semi-major axis increment which reduces the probability of collision to an acceptable level. An inverse dynamic method is employed in the second case study, which addresses an optimal attitude reorientation problem for initial debris tracking. Typical attitude constraints and forbidden pointing zones are considered, highlighting the suitability and general applicability of the proposed DSS functionalities for the intended SBSS mission.