Analysis of adaptive sampling techniques for underwater vehicles
- PDF / 1,403,032 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 0 Downloads / 197 Views
Analysis of adaptive sampling techniques for underwater vehicles Andres Mora · Colin Ho · Srikanth Saripalli
Received: 18 December 2012 / Accepted: 2 May 2013 / Published online: 18 May 2013 © Springer Science+Business Media New York 2013
Abstract A critical problem in planning sampling paths for autonomous underwater vehicles (AUVs) 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 AUVs. 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 costevaluation 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. Keywords Experimental · Optimal · Adaptive sampling · Autonomous underwater vehicles · Kriging
A. Mora (B) · C. Ho · S. Saripalli School of Earth and Space Exploration, Arizona University, ISTB4-BLDG75 781 E. Terrace Rd., Tempe, AZ 85287-6004, USA e-mail: [email protected] C. Ho e-mail: [email protected] S. Saripalli e-mail: [email protected]
1 Introduction Autonomous underwater vehicles (AUVs) are mobile robotic platforms that are utilized for environmental sampling, sensing and are capable of operating in a multitude of underwater aquatic environments (Forrest et al. 2007). In research they are commonly used to carry hydrological, geophysical, and/or biological sensor payloads. These payloads are typically used to gather remote and in situ data to aid the study of open and closed aquatic bodies (Stoker et al. 1996; German et al. 2008). An AUV is generally described as being capable of operation without any human controller; thus it must be able to traverse the aquatic environment on a desired path through autonomous navigation and control (Kumagai et al. 2000). The AUV used to collect the data analyzed in this paper is shown in Fig. 1. The goal of environmental sampling and sensing is to generate an accurate estimate of the underlying scalar field(s) over an area of interest. Generally, an estimate is generated by interpolating the sensing data collected over the area being studied. In the case of aquatic environments, a mobile vehicle can be introduced for it to autonomously collect the necessary sensing data. However a number of questions need to be answered before deciding on a strategy to gather these data. When using an AUV as sensing platform its range is limited by the amount of stored energy. The problem of environm
Data Loading...
