Goal-Oriented Trajectory Refinement for Asteroid Mapping Using Sequential Convex Programming

Operations in proximity of minor bodies demand high levels of autonomy to achieve cost-effective, safe, and reliable solutions. Autonomous path-planning capability plays a pivotal role in this, allowing safe operations in challenging environments. A goal-oriented path-planning methodology is presented here based on repeated trajectory refinement via sequential convex programming. The objective of the approach is to refine an initial guess trajectory to best observe a set of features on the target surface with specific observation requirements while respecting physical and operational constraints. Maneuvering epochs are imposed on a given time horizon to satisfy ground-station requirements, and their magnitude is limited by control capability. Navigation and control uncertainties are taken into account to obtain a robust solution within a modular and flexible implementation framework. The methodology is applied to the case of proximity operations about the Eros asteroid. Results of Monte Carlo analyses are performed with a given set of observation requirements, showing an 80% convergence rate with large improvements in observation capability even starting from a poor initial guess. Overall, this work presents a step toward goal-oriented autonomous guidance capability for small-body proximity operations.