Multi-Disciplinary Design Optimization for Relative Navigation in Non-cooperative Rendezvous

On-orbit applications, such as active debris removal, satellite refueling, maintenance and satellite health diagnosis require the ability for low-cost spacecraft to closely inspect other orbital objects in a non-cooperative manner. During this kind of mission, the relative navigation process becomes of critical importance for guaranteeing safe and collision-free proximity operations and maneuvers. The selection of relative navigation sensors appears being a critical task as it might drive the design choices in other subsystems (e.g., Attitude and orbit control system (AOCS), payload, power supply). This paper aims at studying the application of a Multidisciplinary Design Optimization (MDO) method to the design of AOCS and Navigation subsystems under small satellite constraints. The porposed MDO process is based on the optimization of navigation performance and mass reduction while respecting volume and power constraints for orbital close rendezvous. The navigation chain, including sensor simulation and navigation filter is simulated and integrated into the design cost function. The proposed MDO process based on a Genetic Algorithm (GA) and on an Extended Kalman Filter (EKF) aims at simplifying satellite design by determining a set of optimal admissible sensor combinations despite contradictory objectives on navigation and payload accuracy, mass reduction, power consumption and volume. Possible key advantages of the inclusion of the relative navigation subsystem within MDO process are the reduction of the design process time, the automation and optimization of the navigation architecture while respecting volume and power constraints of small satellites. A demonstration of the effectiveness of the proposed MDO method is provided on the benchmark of AVANTI mission.