Abstract 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.

中文翻译：

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通过深度强化学习方法为行星着陆提供自适应广义 ZEM-ZEV 反馈指导

摘要 大小行星体的精确着陆是未来人类和机器人探索太阳系的一项极为重要的技术。在这种背景下，零努力未命中/零努力速度（ZEM/ZEV）反馈制导算法得到了广泛的研究，并且仍然是一个活跃的研究领域。该算法虽然在准确性和易于实现方面功能强大，但也有一些局限性。因此，在本文中，我们提出了一种基于经典 ZEM/ZEV 的自适应制导算法，其中使用机器学习来克服其局限性，并创建一个闭环制导算法，该算法足够轻量级，可以在航天器上实施，并且足够灵活，能够适应给定的约束场景。