Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited.The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a dynamic "data map" that models a spatiotemporal environment attribute such as ocean salinity content. Our environmental sampling framework consists of two components: to maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; To alleviate the computational bottleneck caused by large-scale data accumulated, we develop a component based on a sparse Gaussian Process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate in learning the environmental data map and efficient in catching up with the dynamic environmental changes † .
Abstract-We present an experimental study of a mechanically scanned profiling sonar for Autonomous Surface Vehicle (ASV) obstacle detection and avoidance. We extract potential obstacles from echo returns and suggest a scanning strategy for sonar in this application. We demonstrate with simulations (driven by data collected in the field) the potential for an ASV to rely solely on sonar data to navigate and avoid obstacles in a lake and harbor environment.
Autonomous Underwater Vehicles (AUVs) are revolutionizing oceanography. Most high-endurance and long-range AUVs rely on satellite phones as their primary communications interface during missions for data/command telemetry due to its global coverage. Satellite phone (e.g., Iridium) expenses can make up a significant portion of an AUV's operating budget during long missions. Slocum gliders are a type of AUV that provide unprecedented longevity in scientific missions for data collection. Here we describe a minimally-intrusive modification to the existing hardware and an accompanying software system that provides an alternative robust disruption-tolerant communications framework enabling cost-effective glider operation in coastal regions. Our framework is specifically designed to address multiple-AUV operations in a region covered by multiple networked base-stations equipped with radio modems. We provide a system overview and preliminary evaluation results from three field deployments using a glider. We believe that this framework can be extended to reduce operational costs for other AUVs during coastal operations.
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