Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number
of places visited during the mission. It depends heavily on the information that is available for the traversed
surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images.
Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute
multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning
algorithm based on the Fast Marching Method is proposed. Environment information coming from different
sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension
is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes
advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance
is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and
field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental
terrain.
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