COMBINA: Relative Navigation for Unknown Uncooperative Resident Space Object

In recent years, space debris has become a threat for satellites operating in Low Earth Orbit. Even by applying debris mitigation guidelines, their number will still increase in the next century. As a consequence, active debris removal missions as well as On-Orbit Servicing missions have gained momentum at both academic and industrial level. The crucial step in both scenarios is the capability of navigating in the neighborhood of the target Resident Space Object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. Several techniques can deal with known and cooperative targets, fewer model-based methods are available if the investigated object is uncooperative (but known), while only a handful of techniques can deal with completely unknown objects, which require building their map while navigating in their neighborhood. The main downside of methods available in literature is the detection and matching of markers from measurements, a step which may severely impair convergence if poorly performed. To overcome said limitations, this paper proposes an hybrid approach for relative navigation at an unknown and uncooperative target resident space object called COarse Model Based relatIve NAvigation: CoMBiNa. The main idea of this algorithm is to combine the advantages of simultaneous localization and mapping with model-based methods by splitting the mission in two phases. During the first phase, the algorithm reconstructs a coarse model of the target. In the second phase, this coarse model is used as the base of a model-based relative navigation technique, effectively shifting the focus towards state and inertia reconstruction. The adopted model-based method avoids searching for exact feature correspondence, thus overcoming the main limitation of current methods. Additionally, this paper proposes a strategy to leverage the structure of the particular model-based navigation method chosen so that measurement outliers can be detected and rejected automatically. To conclude, this paper presents the results of the application of the proposed approach while tested on a generic target model with validation on a limited resource Single Board Computer.