Path planning for asteroid hopping rovers with pre-trained deep reinforcement learning architectures

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.