Analysis of adaptive sampling techniques for underwater vehicles

A critical problem in planning sampling paths for autonomous underwater vehicles is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real-time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for autonomous underwater vehicles. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for high-variance scalar fields for all sampling densities and low-variance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense. Powered by Editorial Manager® and Preprint Manager® from Aries Systems Corporation Autonomous Robots manuscript No. (will be inserted by the editor) Analysis of Adaptive Sampling Techniques for Underwater Vehicles Andres Mora · Colin Ho · Srikanth Saripalli Received: date / Accepted: date Abstract A critical problem in planning sampling paths for autonomous underwater vehicles is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real-time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for autonomous underwater vehicles. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for highvariance scalar fields for all sampling densities and lowvariance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fieldsA critical problem in planning sampling paths for autonomous underwater vehicles is correctly balancing two issues. First, obtaining an accurate scalar field estimation and second, efficiently utilizing the stored energy capacity of the sampling vehicle. Adaptive sampling approaches can only provide solutions when real-time and a priori environmental data is available. In this paper we present an analysis of adaptive sampling methodologies for autonomous underwater vehicles. In particular, we analyze various sampling path strategies including systematic and stratified random patterns within a wide range of sampling densities and their impact in the energy consumption of the vehicle through a cost-evaluation function. Our study demonstrates that a systematic spiral sampling path strategy is optimal for highvariance scalar fields for all sampling densities and lowvariance scalar fields when sampling is sparse. In addition, our results show that the random spiral sampling path strategy is found to be optimal for low-variance scalar fields when sampling is dense.