Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion‐planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and it predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we report results from nearly 300 experimental trials using a planetary rover platform in a Mars‐analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.