Gaussian processes and Fast Marching Square based informative path planning.

The exploration of unknown environments is a challenge in robotics. The proposed method approaches this problem by combining the Fast Marching Square path planning technique with the machine learning method called Gaussian processes (GP). The Fast Marching Square method is used to determine the most unexplored areas of the environment and to plan the path of the vehicle from the current position to the selected point. The GP model is used to obtain predictions about the unexplored regions of the environment based on the collected data so far during the exploration. The use of Unmanned Aerial Vehicles (UAVs) for exploration and surveillance has increased exponentially in the recent years, due to their sensor equipment capabilities and their versatility for flying over difficult terrain. By defining the weight each method has on the selection of the next point to explore, we can focus the UAV on the points with more interesting data defined by the user (i.e. bodies of water), the most unexplored regions, or a combination of both. We present an study on the influence of these weights on the mean absolute error (MAE) and predictive variance obtained from the GP model and test the algorithm on a real environment obtained from a satellite image. We show that we are able to generate an accurate depiction of the environment way faster than traditional methods such as the Boustrophedon.