Space Trajectory Design Using Artificial Intelligence

利用人工智能进行空间轨迹设计

• 批准号: 2887789
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887789
• 关键词: Space; Trajectory; Design; Using; Artificial

Recent research has shown that artificial intelligence can be used to aid the design of space trajectories. Instead of solving a complex and time-consuming optimisation problem, machine learning can be trained to provide an estimate of the cost of a transfer instantaneously. This can be used in preliminary mission design, or in multi-target missions, where the fast computation of a high number of trajectories is necessary. Currently this relies on a database of pre-computed optimal transfers, that can be used to train an artificial neural network.This PhD project will delve into machine learning and reinforcement learning for space trajectory design, with the main aim to create a framework where an agent would automatically learn about optimality of solutions iteratively, taking away the need to perform any optimisation at all. It will also expand to include the selection of possible mission targets (bodies or orbits), and systems design of the spacecraft itself - very often, the design of the trajectory relies on propulsion system, and vice-versa, the selection of propulsion system relies on the trajectory. Integrated mission-system design will be tackled with machine learning.Applications include multi-body missions, where thousands to millions of possible trajectories have to be evaluated, such as interplanetary multi-asteroid rendezvouses, and multiple active debris removal missions. However, fast trajectory optimisation is also used in the preliminary phases of the mission design, where the satellite systems (and propulsion system in particular) are not frozen, and shall be selected and optimised together with the trajectory itself.The ideal candidate will have a background in computer science, artificial intelligence, machine learning, with a strong interest for mathematical modelling and space systems. Alternatively, the candidate can have a background in space systems engineering, with a strong interest (and preferably experience) in artificial intelligence.

最近的研究表明，人工智能可以用来帮助设计空间轨迹。无需解决复杂且耗时的优化问题，机器学习可以被训练为即时提供对传输成本的估计。这可用于初步的使命设计，或在多目标任务，其中快速计算大量的轨迹是必要的。目前，这依赖于一个预先计算的最佳传输数据库，可用于训练人工神经网络。这个博士项目将深入研究空间轨迹设计的机器学习和强化学习，主要目的是创建一个框架，智能体将自动迭代学习解决方案的最优性，从而完全不需要执行任何优化。它还将扩大到包括可能的使命目标（天体或轨道）的选择，以及航天器本身的系统设计-通常，轨道的设计取决于推进系统，反之亦然，推进系统的选择取决于轨道。将利用机器学习进行综合任务系统设计，其应用包括多体任务，其中必须评估数千至数百万条可能的轨道，如行星际多小行星会合和多个主动碎片清除任务。然而，快速轨道优化也用于使命设计的初步阶段，在此阶段，卫星系统（特别是推进系统）没有冻结，应与轨道本身一起选择和优化。理想的候选人应具有计算机科学，人工智能，机器学习的背景，对数学建模和空间系统有浓厚的兴趣。或者，候选人可以有空间系统工程的背景，对人工智能有浓厚的兴趣（最好有经验）。