论文标题
自适应通用ZEM-ZEV反馈指南，通过深入的强化学习方法
Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach
论文作者
论文摘要
精确降落在大型和小行星机构上是对太阳系的未来人类和机器人探索最重要的技术。在这种情况下，对零劳 - 无数/零富效性（ZEM/ZEV）反馈指导算法进行了广泛的研究，并且仍然是一个积极研究的领域。该算法虽然在准确性和易于实施方面有力，但仍有一些局限性。因此，在本文中，我们根据经典的ZEM/ZEV提出了一种自适应指导算法，在该算法中，使用机器学习来克服其局限性并创建封闭的循环指导算法，该算法足够轻巧，可以在船上航天器上实现，并且足够灵活地可以适应给定约束方案。所采用的方法是一种参与者批判性的增强学习算法，该学习算法根据给定的问题约束学习上述指导体系结构的参数。
Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints.
