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

小空间系统的进步使各种在轨服务和装配任务成为可能，例如在轨加油、复杂物体的装配、受损空间系统的救援以及使用多个小空间系统清除空间碎片。随着越来越多的空间系统将用于复杂和关键的在轨服务任务，强大、高效和高度精确的自主近距离机动和对接能力的重要性日益增加。然而，由于不确定性、干扰和多颗卫星的可变操作约束（如执行器饱和、避碰、在可行操作区域范围内以及实现给定编队），自主近距离机动具有挑战性。该项目将为研究人员提供与海军研究生院航天器机器人实验室合作的机会。该合作将包括对使用多个空间系统的在轨服务任务所需的自主近距离机动进行预测和学习路径规划算法的基础和应用研究。这项研究基础设施改进轨道4 EPSCoR研究人员（RII轨道4）项目为新墨西哥州立大学（NMSU）的一名助理教授提供奖学金，并为NMSU的一名研究生提供支持。EPSCoR RII Track-4奖学金项目的主要目标是开发和测试基于鲁棒模型预测控制和强化学习的新型鲁棒制导算法，该算法可以处理用于在轨维修和装配任务的多空间系统编队飞行和近距离机动中的不确定性和干扰。具体来说，该项目将为NMSU团队提供世界一流的设施，使用多个空间系统进行空间接近操作的硬件在环（HIL）模拟，以实时测试和验证所提出的算法。实时计算具有非线性约束的鲁棒模型预测制导和控制的最优解是一个重大的智力挑战。研究人员将开发一种快速优化框架，以解决鲁棒性和预测性优化问题。这项研究还将有助于开发一个强化学习框架，该框架可以学习预测方法的轨迹和控制输入。该奖学金项目将扩大PI的研究能力，并将他的职业道路转向有前途的方向，即指导算法开发和使用多个空间系统的操作。该项目的成果将为在轨服务和装配任务的算法指导做出贡献，并将算法扩展到使用多个空间系统的广泛自主应用中。该项目还将促进NMSU（指定的西班牙裔服务机构）与海军研究生院之间的牢固伙伴关系，以增加在STEM学科中代表性不足的学生的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.