Artificial Potential Fields for Autonomous Cluster Keeping

In recent years the concepts of distributed systems and fractionation spread considerably, as they allow the replacement of a single monolithic spacecraft with multiple smaller ones. The desire for such a design transformation stems from the many limitations that are associated with the traditional monolithic option and can be instead overcome through the use of a fractionated architecture. A cluster of spacecraft working together could enhance the mission performances in many ways, e.g., by augmenting flexibility and redundancy, by reducing costs and risks, by overcoming physical limitations.
On the other hand these benefits come at a price, since the cluster brings a new series of challenges concerning, for example, the sharing of data, the communication, and the relative motion between the objects.
In the field of formation flight, much has been already done in these areas but not everything is directly applicable in a cluster scenario. For what concerns the relative motion, for instance, it is clear that this should always be safe to ensure the success of the mission, but while a formation requires the modules to remain in a precise relative configuration, a cluster only requires satisfaction of minimum and maximum distance constraints to ensure neither that modules drift away, nor that they collide. This peculiar type of requirements on the one side kept boosting the research in the field of the relative motion models, with a particular push in the direction of long-term passive distance-bounded relative orbits, on the other side promoted the development of cluster keeping algorithms, which are desired to be scalable, autonomous and responsive.
In this thesis, at first several types of relative configurations are investigated and compared to observe how they influence the evolution of the relative motion and which advantages they bring into a cluster flight scenario. By considering the number of deployable objects and the v budget required for station keeping as main performance indexes, one specific configuration is selected and further explored with the application of the artificial potential field method. The approach of artificial potentials represents a simple and effective path planner, that can influence the motion of the considered objects, for example to steer them towards goal positions while ensuring obstacle avoidance, by using proper attractive and repulsive behaviours. On top of that the method deals well with the desired requirements of autonomy, responsiveness and scalability, and that is why it has been selected to be applied in the cluster keeping problem.
The artificial potentials are widely studied and applied in the field of robotics, but the complexity of the equations of motion in the orbital environment significantly limited their spread in the space domain. Space research involving this method is nowadays restricted to small-sized relative motion problems, in which the simplified Hill-Clohessy-Wiltshire model could be used. In this thesis, on the other hand, both small- and large-sized clusters are considered, leading to the need for dropping distance-related simplified assumptions and developing a general cluster keeping approach.
Two different artificial-potentials-based architectures are presented, one exploiting the use of virtual reference states that the spacecraft of the cluster track to (indirectly) satisfy minimum and maximum distance constraints, and one dealing directly with the relative distance between the spacecraft to alter their motion and prevent violations of the distance boundaries. By discussing the results of the extensive simulations that have been performed to study the two architectures, the strengths and limitations of these can be highlighted, and eventually a framework employing them both is proposed, showing under which conditions they can be successfully coupled.
Conclusions and recommendations for future work finally wrap up the conducted research and close the thesis.