Conference Paper

Continuous Autonomous Tracking and Imaging of White Sharks and Basking Sharks Using a REMUS-100 AUV

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Abstract-We present results from field experiments in which a REMUS-100 autonomous underwater vehicle (AUV) tracked multiple tagged sharks in the open ocean over periods of several hours. The Oceanographic Systems Laboratory (OSL) developed an algorithm that allows the vehicle to use information from an active transponder to provide a three dimensional track of the animal with high spatial and high temporal resolution. Field studies were conducted in the spring and summer of 2012. Two basking sharks and four white sharks were tagged and tracked for 1-3 hours. Here we present

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... The slow and predictable track trajectories of white sharks compliments surveillance strategies that can make frequent surveillance passes (Figure 3b). However, although white shark behaviour was consistent across the various study locations, it has been demonstrated to significantly differ near abundant food sources, such as in proximity to seal colonies or when whale carcasses wash near shore [13]. Such species-specific information on behaviour can enhance our success of identifying and tracking sharks under different environmental conditions. ...
... In that study, the drone was constrained to the surface, was not equipped with cameras (for behavioural observations), lacked the capacity to monitor animal depth, and resulted in a coarse estimate of the shark's horizontal movements. Packard et al. [13] were the first to mount cameras on an underwater drone for the sole purpose of observing behaviour while actively tracking sharks and collecting environmental data at depth. These authors used a REMUS (Remote Environmental Monitoring UnitS; Woods Hole Oceanographic Institution, Woods Hole, MA, USA) drone, which was developed as a platform for a wide variety of oceanographic instrumentation and outfitted with a Global Positioning System (GPS), wireless communication, iridium capabilities, an inertial navigation system, ring laser gyroscopes to orient the vehicle spatially, and accelerometers to sense changes in speed and velocity [9]. ...
... The drone also carried a variety of sensors including an acoustic Doppler current profiler, a conductivity-temperature probe, magnetic heading sensor, and pressure sensor. During this study, basking and white sharks were tracked at depth off the coast of Cape Cod, MA and direct observations (video) and environmental data were collected, thereby demonstrating that an AUV could actively and accurately track large (>2 m) sharks in shallow waters (<20 m) [13]. ...
Article
Full-text available
Over the past decade, drones have become a popular tool for wildlife management and research. Drones have shown significant value for animals that were often difficult or dangerous to study using traditional survey methods. In the past five years drone technology has become commonplace for shark research with their use above, and more recently, below the water helping to minimise knowledge gaps about these cryptic species. Drones have enhanced our understanding of shark behaviour and are critically important tools, not only due to the importance and conservation of the animals in the ecosystem, but to also help minimise dangerous encounters with humans. To provide some guidance for their future use in relation to sharks, this review provides an overview of how drones are currently used with critical context for shark monitoring. We show how drones have been used to fill knowledge gaps around fundamental shark behaviours or movements, social interactions, and predation across multiple species and scenarios. We further detail the advancement in technology across sensors, automation, and artificial intelligence that are improving our abilities in data collection and analysis and opening opportunities for shark-related beach safety. An investigation of the shark-based research potential for underwater drones (ROV/AUV) is also provided. Finally, this review provides baseline observations that have been pioneered for shark research and recommendations for how drones might be used to enhance our knowledge in the future.
... These technologies can help expand research activities over longer time periods or larger areas, including shallow coastal regions inaccessible by large research vessels (Cokelet et al. 2015, Rudnick 2016. Recently, autonomous underwater vehicles (AUVs) have been used for focal follow type studies of fish, sharks, and turtles (Packard et al. 2013, Skomal et al. 2015, Dodge et al. 2018). These studies, which used acoustic transponders on the targeted animal, were temporally limited (1 to < 8 h) and only able to measure oceanographic parameters, not prey abundance (Packard et al. 2013, Skomal et al. 2015, Dodge et al. 2018. ...
... Recently, autonomous underwater vehicles (AUVs) have been used for focal follow type studies of fish, sharks, and turtles (Packard et al. 2013, Skomal et al. 2015, Dodge et al. 2018). These studies, which used acoustic transponders on the targeted animal, were temporally limited (1 to < 8 h) and only able to measure oceanographic parameters, not prey abundance (Packard et al. 2013, Skomal et al. 2015, Dodge et al. 2018. For focal follow studies with unmanned vehicles to be comparable to traditional survey methods, longer follow durations and direct measurements of prey are needed. ...
... Future studies could incorporate other sources of data, such as acoustic tags similar to those used to track shark, fish, and turtles, to keep the Saildrone in close proximity (e.g. Packard et al. 2013, Skomal et al. 2015. Alternatively, the USV could potentially collect transmitted GPS locations directly from the tracking instrument, bypassing the Argos system (Jeanniard-du-Dot et al. 2017). ...
Article
We tested the feasibility of using Saildrone unmanned wind- and solar-powered surface vehicles to conduct remote focal follow studies of northern fur seals Callorhinus ursinus . Using Argos satellite and transmitted GPS locations, the Saildrones followed a fur seal while recording oceanographic conditions and mapping prey abundance and depth distribution using a scientific echosounder. The Saildrones successfully followed 6 fur seals over 2.4 ± 0.2 d (mean ± SE) and 149.7 ± 16.3 km of the foraging path. Median separation distance between the Saildrone and fur seal path was 0.65 ± 0.1 km and average time separation was 9.9 ± 1.4 h, with minimum time separations ranging from 1.9-4.9 h. Time and distance separation were a function of both animal behavior and study design. Our results show that Saildrones can approach satellite tracked marine predators from long distances and follow them over extended periods while collecting oceanographic and prey data. These successful focal follows demonstrate that unmanned surface vehicles are a valuable tool for collecting data on fine-scale relationships between marine predators, their prey, and the environment.
... Here, we describe a "smart" AUV developed to follow and film free-swimming marine animals, and demonstrate the utility of this technology in a pilot study investigating the subsurface behaviors and habitat of leatherback sea turtles (Dermochelys coriacea) in a high-risk coastal environment. We adapted the SharkCam REMUS-100 AUV developed at the Oceanographic Systems Laboratory (OSL) at the Woods Hole Oceanographic Institution (WHOI) (Packard et al., 2013), and coined it "TurtleCam." TurtleCam's animal-following algorithms were modified to continuously locate, follow and film a tagged turtle while simultaneously collecting environmental data along the turtle's track. ...
... To correctly interpret habitat-driven behaviors, we also need to concurrently observe and sample habitat during behavior studies. Autonomous underwater vehicles can efficiently meet all of these objectives, resulting in a more holistic picture of marine animal behavior (Packard et al., 2013;Kukulya et al., 2015Kukulya et al., , 2016Skomal et al., 2015). The pilot study described here demonstrates proof of concept for using an AUV to study leatherback turtle behavior and habitat in a densely populated, high-risk coastal environment, and it can be easily adapted for other species and habitats with similar conservation concerns. ...
Article
Full-text available
Sea turtles inhabiting coastal environments routinely encounter anthropogenic hazards, including fisheries, vessel traffic, pollution, dredging, and drilling. To support mitigation of potential threats, it is important to understand fine-scale sea turtle behaviors in a variety of habitats. Recent advancements in autonomous underwater vehicles (AUVs) now make it possible to directly observe and study the subsurface behaviors and habitats of marine megafauna, including sea turtles. Here, we describe a “smart” AUV capability developed to study free-swimming marine animals, and demonstrate the utility of this technology in a pilot study investigating the behaviors and habitat of leatherback turtles (Dermochelys coriacea). We used a Remote Environmental Monitoring UnitS (REMUS-100) AUV, designated “TurtleCam,” that was modified to locate, follow and film tagged turtles for up to 8 h while simultaneously collecting environmental data. The TurtleCam system consists of a 100-m depth rated vehicle outfitted with a circular Ultra-Short BaseLine receiver array for omni-directional tracking of a tagged animal via a custom transponder tag that we attached to the turtle with two suction cups. The AUV collects video with six high-definition cameras (five mounted in the vehicle nose and one mounted aft) and we added a camera to the animal-borne transponder tag to record behavior from the turtle's perspective. Since behavior is likely a response to habitat factors, we collected concurrent in situ oceanographic data (bathymetry, temperature, salinity, chlorophyll-a, turbidity, currents) along the turtle's track. We tested the TurtleCam system during 2016 and 2017 in a densely populated coastal region off Cape Cod, Massachusetts, USA, where foraging leatherbacks overlap with fixed fishing gear and concentrated commercial and recreational vessel traffic. Here we present example data from one leatherback turtle to demonstrate the utility of TurtleCam. The concurrent video, localization, depth and environmental data allowed us to characterize leatherback diving behavior, foraging ecology, and habitat use, and to assess how turtle behavior mediates risk to impacts from anthropogenic activities. Our study demonstrates that an AUV can successfully track and image leatherback turtles feeding in a coastal environment, resulting in novel observations of three-dimensional subsurface behaviors and habitat use, with implications for sea turtle management and conservation.
... In their approach, a cylindrical transponder approximately 30 cm long is used as a tag. An ultra-short baseline receiver mounted on the REMUS-100 AUV queries the transponder to determine range and bearing (Packard et al., 2013). Unfortunately, the large size of the transponder greatly limits the use to particularly large marine animals. ...
... While bearing and range measurements obtained from these tags tend to be less accurate than that of a transponder, the small size of these tags makes them applicable to a large variety of smaller fish. Also unlike Packard et al. (2013), our system attempts to minimize any changes in behavior of the shark being tracked by using a controller that circles and maintains a predetermined buffer distance from the shark instead of getting as close as possible. ...
Article
This paper presents a multi-autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo-hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real-time calculation of bearing-to-tag and distance-to-tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi-AUV control system to enable the AUVs to drive toward the estimated target state by following collision-free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real-time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV-obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi-AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.
... One such solution involved the use of a REMUS-100 (Hydroid, Pocasset, MA) AUV proprietary native homing/docking system (Skomal et al. 2015). A short baseline system (SBL) hydrophone array in the nose cone determined the bearing and distance toward a large (~9 cm × 30 cm) native homing beacon, which was on a suitably large great white shark (Carcharadon carcharias) (Packard et al. 2013;Skomal et al. 2015). The AUV continuously turned the vehicle to track the shark, but used the AUV position as proxy for that of the shark (i.e., no tag positions calculated) and required additional assistance from a surface vessel to provide a position (Packard et al. 2013;Kukulya et al. 2016). ...
... A short baseline system (SBL) hydrophone array in the nose cone determined the bearing and distance toward a large (~9 cm × 30 cm) native homing beacon, which was on a suitably large great white shark (Carcharadon carcharias) (Packard et al. 2013;Skomal et al. 2015). The AUV continuously turned the vehicle to track the shark, but used the AUV position as proxy for that of the shark (i.e., no tag positions calculated) and required additional assistance from a surface vessel to provide a position (Packard et al. 2013;Kukulya et al. 2016). The AUV would continuously turn toward the shark and occasionally collided with it (Skomal et al. 2015). ...
Article
Autonomous underwater vehicles (AUVs) have demonstrated superior performance for tracking marine animals tagged with individually coded acoustic transmitters. However, AUVs engaged in mapping the distribution of multiple tagged fish have not previously been able to alter search paths to achieve precise position estimates. This problem is solved by the development of payload control software (Synthetic Aperture Override, SAOVR) that allows the AUV to maneuver with trajectories favorable for solving the tag's location from a synthetic aperture. Upon tag detection during a default mission search path, SAOVR (running on an embedded guest computer) seeks permission to take over navigation from the vehicle's native system after checking constraints of geography, timing, tag identification, signal strength, and current navigation state. Permitted maneuvers are then chosen from a template library and executed before returning the AUV to the point of first deviation for continued searching of other tags. Field evaluation on moored reference tags showed a high level of predictability in the AUV's behavior at SAOVR initiation and through maneuvers. Trials suggest that this logic system is highly beneficial to AUV use for fish telemetry in challenging environments such as narrow, deep fjords, or among reefs. Any mission programmed with the AUV's native software can be run with the SAOVR package to allow scientists to easily implement and manipulate synthetic aperture geometries without altering any of the software. Further modeling can help improve template design specific to expected movements of different fish species and relative to the designation of signal strength‐defined execution thresholds.
... Currently, two Typhoons AUVs are fully operative and already performed many missions at sea: the vehicles are called TifOne and TifTu. TifTu, the AUV exploited during the CommsNet13 sea campaign (Potter (2014)), has a length of 3700 [mm], an external diameter of 350 [mm] and a weight of about 150 [kg] according to the carried payload (the vehicle can be considered an intermediate one compared to the smaller Remus 100 (Packard et al. (2013)) and the bigger Remus 600 (Stokey et al. (2005))). Its autonomy is 8 − 10 [h] and the maximum reachable longitudinal speed is 6 [kn] (whereas the cruise speed is about 2 [kn]). ...
Conference Paper
In this paper, the authors present an underwater navigation system for Autonomous Underwater Vehicles (AUVs) which exploits measurements from an Inertial Measurement Unit (IMU), a Pressure Sensor (PS) for depth and the Global Positioning System (GPS, used during periodic and dedicated resurfacings) and relies on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the state estimation. Both (EKF and UKF) navigation algorithms have been validated through experimental navigation data related to some sea tests performed in La Spezia (Italy) with one of Typhoon class vehicles during the NATO CommsNet13 experiment (held in September 2013) and through Ultra-Short BaseLine (USBL) fixes used as a reference (ground truth). Typhoon is an AUV designed by the Department of Industrial Engineering of the Florence University for exploration and surveillance of underwater archaeological sites in the framework of the Italian THESAURUS project and the European ARROWS project. The obtained results have demonstrated the effectiveness of both navigation algorithms and the superiority of the UKF (very suitable for AUV navigation and, up to now, still not used much in this field) without increasing the computational load (affordable for on-line on-board AUV implementation).
... Currently, two Typhoons AUVs are fully operative and already performed many missions at sea: the vehicles are called TifOne and TifTu. Typhoon class AUVs have a length of 3700 [mm] , an external diameter of 350 [mm] and a weight of about 150 [kg] according to the carried payload (the vehicle can be considered an intermediate one compared to the smaller Remus 100 ( [7]) and the bigger Remus 600 ( [8])). Its autonomy is 8 − 10 [h] and the maximum reachable longitudinal speed is 6 [kn] (whereas the cruise speed is about 2 [kn] ). ...
Conference Paper
Developing reliable navigation strategies is mandatory in the field of Underwater Robotics and in particular for Autonomous Underwater Vehicles (AUVs) to ensure the correct achievement of a mission. Underwater navigation is still nowadays critical, e.g. due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS): an AUV typically proceeds for long time intervals only relying on the measurements of its on-board sensors, without any communication with the outside environment. In this context, the filtering algorithm for the estimation of the AUV state is a key factor for the performance of the system; i.e. the filtering algorithm used to estimate the state of the AUV has to guarantee a satisfactory underwater navigation accuracy. In this paper, the authors present an underwater navigation system which exploits measurements from an Inertial Measurement Unit (IMU), Doppler Velocity Log (DVL) and a Pressure Sensor (PS) for the depth, and relies on either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) for state estimation. A comparison between the EKF approach, classically adopted in the field of underwater robotics and the UKF is given. These navigation algorithms have been experimentally validated through the data related to some sea tests with the Typhoon class AUVs, designed and assembled by the Department of Industrial Engineering of the Florence University (DIEF) for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS and European ARROWS projects. The comparison results are significant as the two filtering strategies are based on the same process and sensors models. At this initial stage of the research activity, the navigation algorithms have been tested offline. The presented results rely on the experimental navigation data acquired during two different sea missions: in the first one, Typhoon AUV #1 navigated in a Remotely Operated Vehicle (ROV) mode near Livorno, Italy, during the final demo of THESAURUS project (held in August 2013); in the latter Typhoon AUV #2 autonomously navigated near La Spezia in the framework of the NATO CommsNet13 experiment, Italy (held in September 2013). The achieved results demonstrate the effectiveness of both navigation algorithms and the superiority of the UKF without increasing the computational load. The algorithms are both affordable for online on-board AUV implementation and new tests at sea are planned for spring 2015.
... In this study, a REMUS-100 AUV (custom built at the WHOI) was modified to locate, follow and videotape a tagged shark as described by Packard et al. (2013). In short, the tracking system consists of a 25 kHz transponder, which is attached to the shark, and the REMUS-100 vehicle, which is rated to a maximum depth of 100 m and equipped with an omnidirectional ultra-short baseline (USBL) array and navigation algorithms to perform three-dimensional autonomous tracking, following and filming of a randomly moving target (i.e. the shark). ...
Article
In this study, an autonomous underwater vehicle (AUV) was used to test this technology as a viable tool for directly observing the behaviour of marine animals and to investigate the behaviour, habitat use and feeding ecology of white sharks Carcharodon carcharias near Guadalupe Island off the coast of Mexico. During the period 31 October to 7 November 2013, six AUV missions were conducted to track one male and three female C. carcharias, ranging in estimated total length (LT) from 3·9 to 5·7 m, off the north-east coast of Guadalupe Island. In doing so, the AUV generated over 13 h of behavioural data for C. carcharias at depths down to 90 m. The sharks remained in the area for the duration of each mission and moved through broad depth and temperature ranges from the surface to 163·8 m depth (mean ± s.d. = 112·5 ± 40·3 m) and 7·9–27·1° C (mean ± s.d. = 12·7 ± 2·9° C), respectively. Video footage and AUV sensor data revealed that two of the C. carcharias being tracked and eight other C. carcharias in the area approached (n = 17), bumped (n = 4) and bit (n = 9) the AUV during these tracks. This study demonstrated that an AUV can be used to effectively track and observe the behaviour of a large pelagic animal, C. carcharias. In doing so, the first observations of subsurface predatory behaviour were generated for this species. At its current state of development, this technology clearly offers a new and innovative tool for tracking the fine-scale behaviour of marine animals.
... Remote environmental monitoring units (REMUS) [35] vehicles are lowcost AUVs originally designed by the Oceanographic Systems Lab to survey, map and travel methodically over an area to sample key ocean characteristics. [36][37][38][39][40] The HUGIN AUVs, with a dual civilian and military application strategy, have been jointly developed by Kongsberg Maritime and the Norwegian Defense Research Establishment. [41,42] Nowadays, the HUGIN family constitutes three basic models: the HUGIN 1000, the HUGIN 3000 and the HUGIN 4500, with the number representing the rated depth from 1000 m to 4500 m. [43][44][45] The Bluefin AUV [46] manufactured by the Bluefin Robotics Corporation is an extremely versatile underwater system that can be utilised for a wide range of missions. ...
... The adopted configuration involves a transceiver (the permanent LOON testbed placed on the seabed or a surface support vehicle ) and a transponder (an acoustic modem) rigidly mounted onboard of the AUV navigating underwater. TifTu, the AUV exploited during the CommsNet13 sea campaign, has a length of 3700 [mm], an external diameter of 350 [mm] and a weight of about 150 [kg] according to the carried payload (the vehicle can be considered an intermediate one compared to the smaller Remus 100 [30] and the bigger Remus 600 [38] ). Its autonomy is 8–10 [h] and the maximum reachable longitudinal speed is 6 [kn] (whereas the cruise speed is about 2 [kn]). ...
Article
Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an Unscented Kalman Filter (UKF). The comparison results to be significant as the two strategies of filtering are based on the same process and sensors models. The UKF-based approach, here adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load. UKF has not yet been extensively used in practical underwater applications, even if it turns out to be quite promising. The proposed results rely on the data acquired during a sea mission performed by one of the two Typhoon class vehicles involved in the NATO CommsNet13 experiment (held in September 2013). As ground truth for performance evaluation and comparison, performed offline, position measurements obtained through Ultra-Short BaseLine (USBL) fixes are used. The result analysis leads to identify both the strategies as effective for the purpose of being included in the control loop of an AUV. The UKF approach demonstrates higher performance encouraging its implementation as a more suitable navigation algorithm even if, up to now, it is still not used much in this field.
... Fusion of USBL measurements with inertial sensors data and/or vehicle dynamics, used for accurate vehicle localization, is shown in [1,2]. In [3] the authors have used USBL to track white sharks with an autonomous underwater vehicle, and in [4] USBL tracking was used to track the diver with an autonomous surface vehicle. ...
Article
Full-text available
In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.
... However, the work has been limited to tracking a single marine target, not entire aggregations. In addition, a single AUV has demonstrated the capability of tracking six sharks simultaneously for a continuous 1-3 hours [23]. However, the AUV was not able to estimate the relative positions of other sharks within the aggregation. ...
Conference Paper
This paper presents a method for modeling and then tracking the 2D planar size, location, orientation, and number of individuals of an animal aggregation using Autonomous Underwater Vehicles (AUVs). It is assumed that the AUVs are equipped with sensors that can measure the position states of a subset of individuals from within the aggregation being tracked. A new aggregation model based on provably stable Markov Process Matrices is shown as a viable model for representing aggregations. Then, a multi-stage state estimation architecture based on Particle Filters is presented that can estimate the time-varying model parameters in real-time using sensor measurements obtained by AUVs. To validate the approach, a historical data set is used consisting of >100 shark trajectories from a leopard shark aggregation observed in the La Jolla, CA coast area. The method is generalizable to any stable group movement model constructed using a Markov Matrix. Simulation results show that, when at least 40+ of sharks are tagged, the estimated number of sharks in the aggregation has an error of 6+. This error increased to 27+ when the system was tested with real data.
... The development of a new generation of small low-cost underwater vehicles (UVs) has begun to enable oceanographic, environmental assessment, and national security missions that were previously considered impractical or infeasible (Clegg and Peterson, 2003;Clem et al., 2012;Corke et al., 2007;Dunbabin et al., 2005;Packard et al., 2013;Steele et al., 2012;Zhou et al., 2014). These small lowcost robotic vehicles commonly employ micro-electro-mechanical systems (MEMS) IMUs comprised of 3-axis MEMS magnetometers, angular rate sensors, and accelerometers to estimate local magnetic heading, pitch, and roll, typically to within several degrees of accuracy, but require careful soft-iron and hard-iron calibration and compensation to achieve these accuracies (Crassidis et al., 2007;Guo et al., 2008;Hamel and Mahony, 2006;Mahony et al., 2008Mahony et al., , 2012Metni et al., 2005Metni et al., , 2006Wu et al., 2015). ...
Preprint
This article addresses the problem of dynamic on-line estimation and compensation of hard-iron and soft-iron biases of 3-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a 3-axis magnetometer and a 3-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating 9 magnetometer bias parameters and 3 angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with 3-axis accelerometer signals, are utilized to estimate bias compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, MD, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy.
... Over the past decade the development of a new generation of small low-cost underwater vehicles (UVs) has begun to enable oceanographic, environmental assessment, and national security missions that were considered impractical or infeasi- ble before (e.g. [3,4,25,34,38]). This new generation of UVs often employ low-cost navigation systems that presently limit them to missions requiring only low-precision navigation of O(1-100)m accuracy when submerged. ...
... AUVs are still prohibitively expensive (~$100,000), however, require large (1.7 kg) tags to be placed onto the tracked animal (to enable the AUV to track the animal autonomously), and have variable tracking accuracy (3 s updates and course changes e.g. Packard et al. (2013); Skomal et al. (2015)). ROVs are more limited than AUVs in their range (< 1000 m compared to hundreds of km), but the direct and immediate control they provide allows more consistent visual tracking of individuals and a greater resolution in the documentation of fine-scale and noteworthy behaviours. ...
Article
While post-release mortality estimates have been conducted on a range of shark species, the short-term sub-lethal effects of capture, handling and release are poorly known and have been mostly investigated in controlled conditions. In addition, the widely accepted notion that immediate post-release active swimming is an indicator of shark condition has never been tested. This study assessed the effects of deck exposure by analysing post-release behaviour of two species of shark, the draughtboard (Cephaloscyllium laticeps) and the piked spurdog (Squalus megalops) in-situ using a remote-operated-vehicle and in a replicated experiment in controlled conditions. In total, 67 sharks were caught with demersal fish trawls and commercial longlines and subjected to different deck exposures and release environments. Tailbeat rates of deck-exposed sharks were significantly lower than the control sharks, but this effect differed between in-situ and experimental environments. Results indicate that capture has species-specific effects, that post-release effects may last longer than 5 min, and that controlled experiments may not be reliable indicators of post-release effects. Immediate post-release swimming was not a good predictor of post-release behaviour, suggesting capture and release fisheries may have significant sub-lethal effects on some species of shark, and that limiting capture or handling time may reduce post-release effects.
... Machine vision processing technology mainly employs cameras and computers to analyze images and provide control information of the AUV drive system. There have been numerous research studies on the application of an AUV equipped with machine vision processing technology, such as the detection of underwater man-made structures and pipeline detection [3][4][5][6][7][8][9][10], auxiliary sonar image navigation [11,12], simultaneous localization and mapping (SLAM) [13][14][15][16][17], obstacle avoidance [18,19], identifying and tracking the habitats of sea animals [20], underwater docking systems [21][22][23][24][25][26][27], and object tracking [28][29][30][31][32][33][34][35][36]. ...
Article
Full-text available
This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed.
Conference Paper
In recent years, great technological subsurface advancements have been made to observe and study Carcharodon carcharias, white sharks with autonomous underwater vehicles (AUVs) [1]. Prior to 2011, tracking pelagic predators like sharks was limited to using active tracking from boats [2] and passive acoustic arrays [3]. These aforementioned techniques proved to be limited by logistics such as weather and boat maneuverability as well as providing poor spatial resolution since fish movements were mimicked by the tracking vessel.
Chapter
Seafloor environments at ever increasing depths on the continental shelf are being resolved at ever higher resolutions as a result of changing sensor technologies and, in part, with the emergence of Autonomous Underwater Vehicles (AUVs) as stable survey platforms. The new age of underwater robots to act as platforms which we can use to deploy sensors to gather information in the ocean is only limited by our imagination. This chapter provides an overview of this technology for applications on the continental shelf. It explores the basic fundamentals of AUV operation and the types of associated instrumentation, the current state of commercial and academic activity and the broad disciplines across which AUVs are currently been employed. AUVs are highly effective tools for sampling in continental shelf marine environments because: (1) they are untethered and can conduct non-destructive sampling in remote habitats (e.g. under ice shelves and over complex terrain) and in depths > 1000 m; (2) they can repeat spatial surveys with a high degree of precision over time; and (3) they can be equipped with a wide range of tools and sensors to sample both physical, chemical and biological data. Unfortunately by the time this chapter is in print, it realistically will already be out of date, as a result of the speed of the technological advancement in this discipline of underwater engineering.
Article
This article reports an adaptive sensor bias observer and attitude observer operating directly on [Formula: see text] for true-north gyrocompass systems that utilize six-degree-of-freedom inertial measurement units (IMUs) with three-axis accelerometers and three-axis angular rate gyroscopes (without magnetometers). Most present-day low-cost robotic vehicles employ attitude estimation systems that employ microelectromechanical system (MEMS) magnetometers, angular rate gyros, and accelerometers to estimate magnetic attitude (roll, pitch, and magnetic heading) with limited heading accuracy. Present-day MEMS gyros are not sensitive enough to dynamically detect the Earth’s rotation, and thus cannot be used to estimate true-north geodetic heading. Relying on magnetic compasses can be problematic for vehicles that operate in environments with magnetic anomalies and those requiring high-accuracy navigation as the limited accuracy ([Formula: see text] error) of magnetic compasses is typically the largest error source in underwater vehicle navigation systems. Moreover, magnetic compasses need to undergo time-consuming recalibration for hard-iron and soft-iron errors every time a vehicle is reconfigured with a new instrument or other payload, as very frequently occurs on oceanographic marine vehicles. In contrast, the gyrocompass system reported herein utilizes fiber optic gyroscope (FOG) IMU angular rate gyro and MEMS accelerometer measurements (without magnetometers) to dynamically estimate the instrument’s time-varying true-north attitude (roll, pitch, and geodetic heading) in real-time while the instrument is subject to a priori unknown rotations. This gyrocompass system is immune to magnetic anomalies and does not require recalibration every time a new payload is added to or removed from the vehicle. Stability proofs for the reported bias and attitude observers, preliminary simulations, and a full-scale vehicle trial are reported that suggest the viability of the true-north gyrocompass system to provide dynamic real-time true-north heading, pitch, and roll utilizing a comparatively low-cost FOG IMU.
Article
This study aims to develop a fuzzy-based visual intelligent guidance system (VIGS) that executes missions involving the identification and tracking of underwater target objects for an autonomous underwater vehicle (AUV). To demonstrate the VIGS functions, a series of tests were conducted in the stability tank and towing tank at National Cheng Kung University. The characteristic of the VIGS is to immediately capture continuous real-time images from the bow to calculate and identify visual information concerning the AUV’s surroundings. By mapping the target’s information in the AUV visual coordinate system (two-dimensional) onto the earth-fixed coordinates (three-dimensional), the relationships of corresponding distance and azimuth between the target and the AUV were defined. Eventually, this information was used to calculate the control parameters of the AUV’s moving speed and heading angle, completing the visual motion control framework. In the dynamic guidance experiments, the moving speed of the target object is proved to be a predominant factor leading to different trends of AUV’s steering performances for pitch and yaw controls.
Article
Full-text available
A review of past behavioral ultrasonic telemetry studies of sharks and rays is presented together with previously unpublished material on the behavior of the lemon shark, Negaprion brevirostris, around the Bimini Islands, Bahamas. The review, focusing on movement behaviors of 20 shark and three ray species, reveals that elasmobranchs exhibit a variety of temporal and spatial patterns in terms of rates-of-movement and vertical as well as horizontal migrations. The lack of an apparent pattern in a few species is probably attributable to the scarcity of tracking data. Movements are probably governed by several factors, some still not studied, but data show that food, water temperature, bottom type, and magnetic gradient play major roles in a shark's decision of where and when to swim. A few species exhibit differences in behavior between groups of sharks within the same geographical area. This interesting finding warrants further research to evaluate the causes of these apparent differences and whether these groups constitute different subpopulations of the same species. The lack of telemetry data on batoids and some orders of sharks must be addressed before we can gain a more comprehensive understanding of the behavior of elasmobranch fishes. Previously unpublished data from 47 smaller and 38 larger juvenile lemon sharks, collected over the decade 1988–1998, provide new results on movement patterns, habitat selection, activity rhythms, swimming speed, rate-of-movement, and homing behavior. From these results we conclude that the lemon shark is an active predator with a strong, apparently innate homing mechanism. This species shows ontogenetic differences in habitat selection and behavior, as well as differences in movements between groups of individuals within the same area. We suggest three hypotheses for future research on related topics that will help to understand the enigmatic behavior of sharks.
Article
The sand tiger shark Carcharias taurus is a large coastal species that has endured marked declines in its western North Atlantic population over the past 30 yr. In the face of these declines, identification of nursery areas for this species is of particular importance to ensure the implementation of protective measures that will maximize survival of young individuals to maturity. Passive acoustic telemetry was used to assess the emergence of Plymouth, Kingston, Duxbury (PKD) Bay, Massachusetts, USA, as a seasonal nursery for juvenile sand tigers that migrate north from southern parturition grounds. Seasonal residency, habitat use, and site fidelity of 73 acoustically tagged juvenile sand tigers (78 to 108 cm fork length) were monitored within PKD Bay during 4 seasonal periods from 2008 to 2011. Eight individuals were tracked in multiple years, with 2 individuals returning to PKD Bay in 3 consecutive years. Sand tigers remained in PKD Bay for periods of 1 to 124 d and displayed a high degree of site fidelity to 2 core habitats during each year of the study. Weekly activity space estimates were relatively constant throughout each yearly monitoring period, with a general increase prior to emigration of sharks from the embayment. Emigration of sharks from PKD Bay was significantly related to both day length and water temperature. Collectively, these results suggest that PKD Bay constitutes a seasonal nursery area for juvenile sand tigers and warrants the extension of juvenile sand tiger essential fish habitat north of Cape Cod, Massachusetts, USA.