Autonomous navigation & mapping of small bodies

Abstract

Autonomous mapping and navigation around unknown small bodies is a challenging problem. In todays missions, small body mapping and navigation (SBMN) require significant human intervention on the ground for map refinement and supervision of the navigation and orbit selection process. Although current methodologies adequately performed in past missions (e.g., Rosetta, Hayabusa, Deep Space), they are not suitable for applications requiring a high level of autonomy. This work proposes a method for autonomous orbit selection and adaptation around a small body while mapping its surface. In particular, in this work, we will develop cost functions that quantify the orbit goodness in the sense of map improvement. In other words, we develop quantitative measures that characterize the accuracy of the small body map and use these measures in an optimization process to compute the next best orbit that maximally contributes to the map enhancement. The proposed framework reduces the human involvement in this process and takes a step toward the fully autonomous mapping and navigation around small bodies.