Abstract Asteroid surface exploration is challenging due to complex terrain topology and irregular gravity field. A hopping rover is considered as a promising mobility solution to explore the surface of small celestial bodies. Conventional path planning tasks, such as traversing a given map to reach a known target, may become particularly challenging for hopping rovers if the terrain displays sufficiently complex 3-D structures. As an alternative to traditional path-planning approaches, this work explores the possibility of applying deep reinforcement learning (DRL) to plan the path of a hopping rover across a highly irregular surface. The 3-D terrain of the asteroid surface is converted into a level matrix, which is used as an input of the reinforcement learning algorithm. A deep reinforcement learning architecture with good convergence and stability properties is presented to solve the rover path-planning problem. Numerical simulations are performed to validate the effectiveness and robustness of the proposed method with applications to two different types of 3-D terrains.

中文翻译：

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具有预训练深度强化学习架构的小行星跳跃漫游车路径规划

摘要 由于地形复杂、重力场不规则，小行星表面探测具有挑战性。跳跃式漫游车被认为是探索小天体表面的一种很有前途的移动解决方案。如果地形显示足够复杂的 3D 结构，传统的路径规划任务，例如遍历给定地图以到达已知目标，对于跳跃式漫游车来说可能变得特别具有挑战性。作为传统路径规划方法的替代方案，这项工作探索了应用深度强化学习 (DRL) 来规划漫游车在高度不规则表面上的路径的可能性。小行星表面的 3-D 地形被转换成一个等级矩阵，作为强化学习算法的输入。提出了一种具有良好收敛性和稳定性的深度强化学习架构来解决流动站路径规划问题。进行数值模拟以验证所提出方法的有效性和鲁棒性，并应用于两种不同类型的 3-D 地形。