RII Track-4 NSF: Robust, Predictive, and Learning Guidance Algorithms for On-Orbit Servicing and Assembly Using Multiple Space Systems

Advancements in small space systems enable a variety of on-orbit servicing and assembly missions, such as on-orbit refueling, assembly of complex objects, rescue of damaged space systems, and space debris removal using multiple small space systems. As more space systems will be utilized for sophisticated and critical on-orbit servicing missions, the importance of robust, efficient, and highly accurate autonomous close-proximity maneuvering and docking capability is growing.

However, autonomous close-proximity maneuvering is challenging due to uncertainty, disturbances, and multiple satellites’ variable operational constraints, such as actuator saturation, collision avoidance, being within the bounds of the feasible operating region, and achieving a given formation. This project will provide an opportunity for the researcher to collaborate with the Spacecraft Robotics Laboratory at the Naval Postgraduate School.

This collaboration will include performing basic and applied research on predictive and learning path-planning algorithms for autonomous close-proximity maneuvering required in on-orbit servicing missions using multiple space systems.

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project provides a fellowship to an Assistant professor at New Mexico State University (NMSU) and support for an NMSU graduate student. The primary goal of this EPSCoR RII Track-4 fellowship project is to develop and test novel robust guidance algorithms based on robust model predictive control and reinforcement learning that can deal with uncertainty and disturbances in formation flying and proximity maneuvering of multiple space systems for on-orbit servicing and assembly missions.

Specifically, this project will offer the NMSU team access to world-class facilities for hardware-in-the-loop (HIL) simulation of space proximity operations using multiple space systems to test and validate the proposed algorithms in real-time. Computing the optimal solutions for robust model predictive guidance and control with nonlinear constraints in real time is a significant intellectual challenge.

Researchers will develop a rapid optimization framework to address this challenge in solving robust and predictive optimization problems. This research will also contribute to developing a reinforcement learning framework that learns trajectories and control inputs of the predictive approaches. This fellowship project will expand the PI’s research capacity and transform his career path towards a promising direction in guidance algorithm development and in operations using multiple space systems.

The outcomes of this project will contribute algorithmically to guidance of on-orbit servicing and assembly missions, and extend the algorithms to a wide range of autonomous applications using multiple space systems. This project will also foster a strong partnership between NMSU, a designated Hispanic-Serving Institution, and the Naval Postgraduate School to increase the participation of students underrepresented in STEM disciplines.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.