Data-driven selective sampling for marine vehicles using multi-scale paths

This paper addresses adaptive coverage of a spatial field without prior knowledge. Our application in this paper is to cover a region of the sea surface using a robotic boat, although the algorithmic approach has wider applicability. We propose an anytime planning technique for efficient data gathering using point-sampling based on non-uniform data-driven coverage. Our goal is to sense a particular region of interest in the environment and be able to reconstruct the measured spatial field. Since there are autonomous agents involved, there is a need to consider the costs involved in terms of energy consumed and time required to finish the task. An ideal map of the scalar field requires complete coverage of the region, but can be approximated by a good sparse coverage strategy along with an efficient interpolation technique. We propose to optimize the trade off between the environmental field mapping and the costs (energy consumed, time spent, and distance traveled) associated with sensing. We present an anytime algorithm for sampling the environment adaptively by following a multi-scale path to produce a variable resolution map of the spatial field. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a spatial field spending minimum energy. We present results that indicate our sampling technique gathering most informative samples with least travel. We validate our approach through simulations and test the system on real robots in the open ocean.