EAPSI: A Machine Learning Approach to Lunar Spacecraft Trajectory Optimization

这项研究将在日本宇宙航空研究开发机构（JAXA）与Yasuhiro Kawakatsu博士合作，研究创新的航天器轨迹优化方法，用于即将到来的月球小型航天器任务EQUULEUS，该任务将于2018年底由美国宇航局的太空发射系统（SLS）火箭发射。这一发现可能使曾经被认为不可能的太空任务成为可能，为科学收集带来更多奇特而令人兴奋的机会。这项任务还将通过对地球等离子层进行成像和测量其分布，进一步加深科学家对地球周围辐射环境的了解，这可能为在长途太空旅行中保护人类和电子设备免受辐射损害提供重要的见解。这项研究探索了变革性的概念，将机器学习和轨迹优化结合在一起，这两个主题在很大程度上尚未被探索。与JAXA的合作是进一步研究的独特机会，因为它是公认的轨道设计领导者，并且在低推力和低能航天器方面拥有丰富的任务经验。航天器将通过地月轨道进入地月系统L2拉格朗日点附近的稳定轨道，通过低能路径利用地月系统有效势能的拓扑稳定性。传统的轨迹优化方法（即直接法和间接法）会产生大量的最优控制轨迹数据集。一旦生成状态控制对数据集，将在该数据集上训练人工神经网络（ANN）。通过训练，人工神经网络开发出可实时实施的空间控制策略。航天器，在任何时刻，将感知其环境，并采取相应的行动（即节流其推进器）。这种控制方法类似于自然界中生物体的行为。就像一只简单的家蝇能够导航到它的食物来源，实时做出决定一样，航天器在试图实现目标时也应该能够做到这一点。该奖项由美国国家科学基金会和日本科学促进会共同资助，隶属于东亚和太平洋暑期研究所项目，支持一名美国研究生进行暑期研究。

This research will investigate innovative spacecraft trajectory optimization methods at the Japanese Aerospace Exploration Agency (JAXA) in collaboration with Dr. Yasuhiro Kawakatsu for the upcoming lunar small-spacecraft mission, EQUULEUS, which will be launched aboard NASA's Space Launch System (SLS) rocket at the end of 2018. The findings may enable space missions that were once thought to be impossible, leading to more exotic and exciting opportunities for science collection. This mission will also further scientists understanding of the radiation environment surrounding Earth by imaging its plasmasphere and measuring its distribution, which may provide important insight for protecting both humans and electronics from radiation damage during long space journeys. This research explores transformative concepts, combining machine learning and trajectory optimization, two subjects which, in combination, have been largely unexplored. Collaboration with JAXA is a unique opportunity to further this research, as it is a recognized trajectory design leader and has extensive mission experience with low-thrust and low-energy spacecraft. The spacecraft will insert itself into a stable orbit about the L2 Lagrange point of the Earth-Moon system through a cislunar trajectory, exploiting the topological stability of the Earth-Moon system's effective potential through low-energy pathways. A large data set of optimal control trajectories will be generated through conventional trajectory optimization methods (i.e. direct methods and indirect methods). Once the data set of state-control pairs is generated, an artificial neural network (ANN) will be trained on the data set. Through training, the ANN develops a spatial control policy that can be implemented in real-time. The spacecraft, at any moment in time, will perceive its environment and take actions (i.e. throttle its thrusters) accordingly. This control method is analogous to how organisms behave in nature. Just as a simple house fly is able to navigate to its food source, making decisions in real-time, a spacecraft should be able to do the same when trying to achieve its objective.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.