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.
Highlights Deep reinforcement learning is proposed for planning the path of hopping rovers over irregular asteroid surfaces. An equivalent conversion method reduces the solution space dimension by transforming 3-D terrain maps into reward matrices. Advanced agent architectures are employed to improve learning efficiency.
Path planning for asteroid hopping rovers with pre-trained deep reinforcement learning architectures
Acta Astronautica ; 171 ; 265-279
2020-03-07
15 pages
Article (Journal)
Electronic Resource
English
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