A Peak-Capture Algorithm Used on an Autonomous Underwater Vehicle in the 2010 Gulf of Mexico Oil Spill Response Scientific Survey • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Yanwu Zhang, Robert S. McEwen, John P. Ryan, James G. Bellingham, and Hans Thomas Monterey Bay Aquarium Research Institute, 7700 Sandholdt Road, Moss Landing, California 95039 e-mail: yzhang@mbari.org Charles H. Thompson Southeast Fisheries Science Center, National Oceanographic and Atmospheric Administration, Building 1103, Room 218, Stennis Space Center, Mississippi 39529 Erich Rienecker Monterey Bay Aquarium Research Institute, 7700 Sandholdt Road, Moss Landing, California 95039 Received 11 January 2011; accepted 10 May 2011 During the Gulf of Mexico Oil Spill Response Scientific Survey on the National Oceanic and Atmospheric Ad- ministration Ship Gordon Gunter Cruise GU-10-02 (27 May–4 June 2010), a Monterey Bay Aquarium Research Institute autonomous underwater vehicle (AUV) was deployed to make high-resolution surveys of the water column in targeted areas. There were 10 2-liter samplers on the AUV for acquiring water samples. An essential challenge was how to autonomously trigger the samplers when peak hydrocarbon signals were detected. In ship hydrocasts (measurements by lowered instruments) at a site to the southwest of the Deepwater Horizon wellhead, the hydrocarbon signal showed a sharp peak between 1,100- and 1,200-m depths, suggesting the existence of a horizontally oriented subsurface hydrocarbon plume. In response to this finding, we deployed the AUV at this site to make high-resolution surveys and acquire water samples. To autonomously trigger the samplers at peak hydrocarbon signals, we modified an algorithm that was previously developed for capturing peaks in a biological thin layer. The modified algorithm still uses the AUV’s sawtooth (i.e., yo-yo) trajectory in the vertical dimension and takes advantage of the fact that in one yo-yo cycle, the vehicle crosses the horizontal plume (i.e., the strong-signal layer) twice. On the first crossing, the vehicle detects the peak and logs the corre- sponding depth (after correcting for the detection delay). On the second crossing, a sampling is triggered when the vehicle reaches the depth logged on the first crossing, based on the assumption that the depth of the hori- zontal oil layer does not vary much between two successive crossings that are no more than several hundred meters apart. In this paper, we present the algorithm and its performance in an AUV mission on 3 June 2010 in the Gulf of Mexico. In addition, we present an improvement to the algorithm and the corresponding results from postprocessing the AUV mission data. Published 2011 Wiley Periodicals, Inc. ∗ 1. INTRODUCTION The largest offshore oil spill in history began in late April 2010 following an explosion on the Deepwater Horizon drilling platform in the Gulf of Mexico. Total destruction of the drilling platform and loss of control over the well- head 1,500 m below the sea surface initiated a massive en- vironmental disturbance. Early observations from ships in- dicated that large plumes of oil, dispersed at the wellhead itself, were remaining in the deep ocean rather than rising to the surface. However, the existence of deep oil plumes was highly controversial. This controversy, and the need to map and sample such plumes if found, entrained the application of a Monterey Bay Aquarium Research Insti- tute (MBARI) autonomous underwater vehicle (AUV) dur- ing the Gulf of Mexico Oil Spill Response Scientific Survey on the National Oceanic and Atmospheric Administration (NOAA) Ship Gordon Gunter Cruise GU-10-02 (27 May– 4 June 2010). As shown in Figure 1, the Dorado AUV (Bellingham, Streitlien, Overland, Rajan, Stein, et al., 2000; Sibenac, Kirkwood, McEwen, Shane, Henthorn, et al., 2002) has a length of 4.2 m and a diameter of 0.53 m at the midsec- tion. It is propeller driven, with a typical speed of about 1.5 m/s. On Cruise GU-10-02, the vehicle’s sensor suite in- cluded Sea-Bird SBE3 temperature and SBE4 conductivity sensors, a Paroscientific 8CB4000-I pressure sensor, a HOBI Labs HydroScat-2 sensor for measuring chlorophyll fluo- rescence at 700-nm wavelength and optical backscatter at 420- and 700-nm wavelengths, a Sea-Bird SBE43 oxygen sensor, and a WET Labs ECO-FL sensor (WET Labs, 2009) for measuring colored dissolved organic matter (CDOM) Journal of Field Robotics 28(4), 484–496 (2011) Published 2011 Wiley Periodicals, Inc. ∗ This article is a US Government work and, as such, is in the public domain of the United States of America. View this article online at wileyonlinelibrary.com • DOI: 10.1002/rob.20399