Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models

Robot navigation in outdoor environments is exposed to detrimental factors such as vibrations or power consumption due to the different terrains on which the robot navigates. In this paper, we address the problem of actively improving navigation by planning paths that aim at reducing over time phenomena such as vibrations during traversal. Our approach uses a Gaussian Process (GP) mixture model and an aerial image of the environment to learn and improve continuously a place-dependent model of such phenomena from the experiences of the robot. We use this model to plan paths that trade-off the exploration of unknown promising regions and the exploitation of known areas where the impact of the detrimental factors on navigation is low, leading to an improved navigation over time. We implemented our approach and thoroughly tested it using real-world data. Our experiments suggest that our approach with no initial information leads the robot, after few runs, to follow paths along which it experiences similar vibrations or energy consumption as if it was following the optimal path computed given the ground truth information.