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Using an Autonomous Underwater Vehicle to Track the Thermocline Based on Peak-Gradient Detection Yanwu Zhang, Senior Member, IEEE, James G

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Using an Autonomous Underwater Vehicle to Track the Thermocline Based on Peak-Gradient Detection Yanwu Zhang, Senior Member, IEEE, James G 544 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 37, NO. 3, JULY 2012 Peer-Reviewed Technical Communication Using an Autonomous Underwater Vehicle to Track the Thermocline Based on Peak-Gradient Detection Yanwu Zhang, Senior Member, IEEE, James G. Bellingham,Member,IEEE, Michael A. Godin, and John P. Ryan Abstract—The thermocline plays a key role in underwateracous- visually identify the thermocline. Then, we set a narrowtem- tics and marine ecology. In oceanographic surveys, it is often desir- perature envelope (i.e., lower and upper temperature bounds) able to detect the thermocline and track its spatio–temporal varia- around the thermocline, and commanded the vehicle to run tion. Mobility of an autonomous underwater vehicle (AUV) makes it an efficient platform for thermocline tracking. In this paper, we within the temperature envelope, thereby closely tracking the present an autonomous algorithm for detecting and tracking the thermocline. This method was not fully autonomous because it thermocline by an AUV. The key is detection and close tracking of required human intervention (i.e., data analysis) in a prelimi- the maximum vertical gradient of temperature. On August 31 and nary survey to set temperature bounds for thermocline tracking. September 1, 2010, the Tethys AUV ran the algorithm to closely In some other experiments that used AUVs for thermocline track the thermocline across a sharp temperature front in Mon- terey Bay, CA. tracking [5], [6], human intervention was required to define a preset threshold of the vertical temperature gradient. Index Terms—Autonomous underwater vehicle (AUV), Petillo et al. presented an autonomous thermocline-tracking peak-gradient detection, thermocline. algorithm for an AUV [7]. The vehicle first conducts a deep dive and calculates the average vertical gradient of temperature over the full depth. Then, the layer where the temperature gradient exceeds the full-depth average temperature gradient is defined I. INTRODUCTION as the thermocline layer. The AUV subsequently confines its sawtoothtrajectoryinthevertical dimension (i.e., a yo-yo tra- HE depth at which the vertical temperature gradient is jectory) within this layer in an attempt to track the thermocline. maximum is called the thermocline [1]. The thermocline T In [8] and [9], Cruz and Matos took a major step forward is of great significance to the physical, chemical, and biological in using an AUV to autonomously track the thermocline. The functioning of marine environments. Temperature is a domi- temperature profile was properly modeled as being composed nant determinant of underwater sound speed. The direction of of three layers (from shallow to deep): the upper mixed layer sound propagation depends on the sound-speed profile. Hence, with a low vertical temperature gradient, the thermocline layer the thermocline depth and the shape of the temperature profile with a high vertical temperature gradient, and the deep-water determine the bending of the sound rays, which affects the layer with a low vertical temperature gradient. Suppose the sonar performance [2]. The thermocline also plays a key role in AUVrunsonayo-yotrajectory in the vertical dimension and marine ecology. The common coincidence of the thermocline is currently on an ascent leg. The vehicle records the maximum (for temperature) and the nutricline (for nutrient concentration) temperature gradient on this ascent leg. Three defines a physical–chemical structure that regulates the ocean thresholds of temperature gradient are accordingly defined: carbon cycle [3]. In oceanographic surveys, it is often desirable , , to detect the thermocline and track its spatio–temporal varia- (the coefficients can be set tion. Cazenave et al. (including the first and second coauthors to different values depending on the water column’s prop- of this paper) have previously developed a method of using erties). On the succeeding descent leg, when the measured an autonomous underwater vehicle (AUV) to closely track the temperature gradient exceeds , the AUV will assume it vertical displacement of the thermocline, which was success- has exited the upper mixed layer and entered the thermocline fully applied in investigating internal tidal waves in Monterey layer. The vehicle continues to dive, and when the temperature Bay, CA, in 2007 [4]. We first deployed the vehicle on a short gradient drops below , the AUV will assume it has mission to measure the vertical temperature profile, and then exited the thermocline layer and entered the deep-water layer. recovered the AUV to quickly review the measured profile and At this point, the vehicle flips attitude to ascent. Also at this point, the vehicle records the maximum temperature gradient Manuscript received August 05, 2011; revised December 23, 2011 and March on the just completed descent leg, and accord- 17, 2012; accepted March 20, 2012. Date of publication May 15, 2012; date of current version July 10, 2012. This work was supported by the David and Lucile ingly set the three thresholds of temperature gradient , Packard Foundation. ,and for the upcoming ascent leg. On the Associate Editor: R. Eustice. ascent leg, when the measured temperature gradient exceeds The authors are with the Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039 USA (e-mail: [email protected]). ,theAUV will assume it has exited the deep-water layer Digital Object Identifier 10.1109/JOE.2012.2192340 and entered the thermocline layer. The vehicle continues to 0364-9059/$31.00 © 2012 IEEE ZHANG et al.: USING AN AUTONOMOUS UNDERWATER VEHICLE TO TRACK THE THERMOCLINE BASED ON PEAK-GRADIENT DETECTION 545 climb, and when the temperature gradient drops below , B. Detecting the Peak Vertical Gradient of Temperature the AUV will assume it has exited the thermocline layer and WeruntheAUVonayo-yotrajectory(inthevertical entered the upper mixed layer. At this point, the vehicle flips dimension) that is defined by successive target depths (as attitude to descent. The AUV tracks the thermocline by alter- will be described in Section II-D). On each descent or as- nating the descent and ascent behaviors. Depth limits are set cent leg, the AUV seeks the maximum vertical gradient of for vehicle safety. temperature and the corresponding depth We have independently developed an autonomous algorithm (regarded as the thermocline depth) as for an AUV to detect and track the thermocline [10]. We present follows (both variables are initialized to zero): our method in Section II, and also compare it with Petillo’s and Cruz’s methods. On August 31 and September 1, 2010, the If Tethys AUV [11] ran the presented algorithm to closely track set to the thermocline across a sharp temperature front in Monterey and set to (2) Bay, CA, as described in Section III. We propose future work in Section IV. Sincethedifferentiationin(1)isconductedonthe th and th depth bins, we take [i.e., the depth of the last sample in the th bin] as the thermocline II. A METHOD FOR DETECTING AND TRACKING THE depth. When the AUV turns from descent to ascent (or from THERMOCLINE BASED ON PEAK-GRADIENT DETECTION ascent to descent), it reports of the entire de- The mechanism of our method is as follows. scent (or ascent) leg it has just completed and the corresponding 1) The AUV detects the maximum vertical gradient of tem- . At the start of the next ascent (or descent) perature on each descent or ascent leg on a yo-yo trajectory. leg, peak-gradient detection starts anew. The corresponding depth is regarded as the thermocline depth. C. Tracking the AUV’s Descent and Ascent 2) The AUV sets the target depth for the upcoming ascent On each descent or ascent leg, the AUV seeks the peak or descent leg to vertical temperature gradient along the entire leg, and reports where is an extension depth and the corresponding to ensure that the AUV crosses the thermocline with some on completion of the leg. Hence, it is a key task to keep track extra depth range. When the vehicle reaches the target of the start and end of a descent or ascent leg. Following depth on an ascent (or descent) leg, it flips attitude to our previously developed algorithm for capturing peaks in a descent (or ascent). biological thin layer [12], we define a state variable 3) The AUV repeats the above descent and ascent behaviors ( : descending; : ascending), and two so as to closely track the thermocline. accompanying variables: the maximum depth (for The key components of the algorithm are given as follows. descent) and the minimum depth (for ascent), as expressed in (3), shown at the top of the next page. A. Averaging Temperature in Depth Bins is the AUV’s depth at the current time [note that is a positive number increasing with depth]. is initialized The vertical gradient of temperature is the partial to zero; at the attitude flip from descent to ascent, derivative of temperature as a function of depth (a pos- will be set to the flipping-point depth. When the AUV turns itive number increasing with depth). This differentiation am- from descent to ascent, flips from 1 to 0. Conversely, plifies temperature measurement noise as well as temperature’s when the AUV turns from ascent to descent, flips from fine-scale variation over depth. To mitigate this effect, we divide 0 to 1. To prevent false state changes due to the depth sensor’s the water column into a number of depth bins, and average the measurement noise, we set a threshold 0.5 m. At the AUV’s temperature measurements in each bin. The averaged end of a descent leg, only when the depth has decreased four temperature in each bin is used for calculating the vertical times in a row and also has decreased from by more gradient of temperature than ,does flip from 1 to 0. The requirement of four consecutive depth decrements will cause some delay in (1) detecting the AUV’s attitude change, which means that a short starting segment (near the attitude flip point) of the ensuing where is the depth bin size, and is the depth bin index.
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