Ground Interaction Models for Increased Autonomy of Planetary Exploration Systems

Abstract Future planetary exploration robots need to improve their autonomy to increase mission safety and efficiency. The presented concept achieves this by introducing the learning and usage of ground interaction models, which allow a more precise modelling of the robot’s mobility on different terrains. The idea is that a precise prediction of the expected performance will, on the one hand, allow an early detection of changed conditions and, on the other hand, enable a system to appropriately react on it. By classifying the traversed terrain, a robot gains the possibility to replan its path or to change its locomotion behavior to eventually optimize mission success. The paper provides details, on how the ground interaction models are trained, how the required data is collected, and how they are embedded into a physical simulator to use them online on the system.