Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery

Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2007.Includes bibliographical references (p. 313-325).This thesis presents a stochastic mapping framework for autonomous robotic chemical plume source localization in environments with multiple sources. Potential applications for robotic chemical plume source localization include pollution and environmental monitoring, chemical plant safety, search and rescue, anti-terrorism, narcotics control, explosive ordinance removal, and hydrothermal vent prospecting. Turbulent flows make the spatial relationship between the detectable manifestation of a chemical plume source, the plume itself, and the location of its source inherently uncertain. Search domains with multiple sources compound this uncertainty because the number of sources as well as their locations is unknown a priori. Our framework for stochastic mapping is an adaptation of occupancy grid mapping where the binary state of map nodes is redefined to denote either the presence (occupancy) or absence of an active plume source. A key characteristic of the chemical plume source localization problem is that only a few sources are expected in the search domain. The occupancy grid framework allows for both plume detections and non-detections to inform the estimated state of grid nodes in the map, thereby explicitly representing explored but empty portions of the domain as well as probable source locations.(cont.) However, sparsity in the expected number of occupied grid nodes strongly violates a critical conditional independence assumption required by the standard Bayesian recursive map update rule. While that assumption makes for a computationally attractive algorithm, in our application it results in occupancy grid maps that are grossly inconsistent with the assumption of a small number of occupied cells. To overcome this limitation, several alternative occupancy grid update algorithms are presented, including an exact solution that is computationally tractable for small numbers of detections and an approximate recursive algorithm with improved performance relative to the standard algorithm but equivalent computational cost. Application to hydrothermal plume data collected by the autonomous underwater vehicle ABE during vent prospecting operations in both the Pacific and Atlantic oceans verifies the utility of the approach. The resulting maps enable nested surveys for homing-in on seafloor vent sites to be carried out autonomously. This eliminates inter-dive processing, recharging of batteries, and time spent deploying and recovering the vehicle that would otherwise be necessary with survey design directed by human operators.by Michael V. Jakuba.Ph.D

A novel trigger-based method for hydrothermal vents prospecting using an autonomous underwater robot

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Autonomous Robots 29 (2010): 67-83, doi:10.1007/s10514-010-9187-y.In this paper we address the problem of localizing active hydrothermal vents on the seafloor using an Autonomous Underwater Vehicle (AUV). The plumes emitted by hydrothermal vents are the result of thermal and chemical inputs from submarine hot spring systems into the overlying ocean. The Woods Hole Oceanographic Institution's Autonomous Benthic Explorer (ABE) AUV has successfully localized previously undiscovered hydrothermal vent fields in several recent vent prospecting expeditions. These expeditions utilized the AUV for a three-stage, nested survey strategy approach (German et al., 2008). Each stage consists of a survey flown at successively deeper depths through easier to detect but spatially more constrained vent fluids. Ideally this sequence of surveys culminates in photographic evidence of the vent fields themselves. In this work we introduce a new adaptive strategy for an AUV's movement during the first, highest-altitude survey: the AUV initially moves along pre-designed tracklines but certain conditions can trigger an adaptive movement that is likely to acquire additional high value data for vent localization. The trigger threshold is changed during the mission, adapting the method to the different survey profiles the robot may find. The proposed algorithm is vetted on data from previous ABE missions and measures of efficiency presented

Ocean robots uncover microbial secrets

Life on Earth began in the sea, and the oceans continue to support life on our planet. Of particular importance is the ability of marine microbes to exist in a complex web of relationships where substances are continually transformed and exchanged

Clio: An Autonomous Vertical Sampling Vehicle for Global Ocean Biogeochemical Mapping

We report the design, sea trials, and scientific operation of a fast vertical profiling autonomous underwater vehicle, called Clio, designed to cost-effectively improve the understanding of marine microorganism ecosystem dynamics on a global scale by collecting high-volume filter samples autonomously, in contrast to conventional techniques that require a ship’s wire

Autonomous and remotely operated vehicle technology for hydrothermal vent discovery, exploration, and sampling

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 1 (2007): 152-161.Autonomous and remotely operated underwater vehicles play complementary roles in the discovery, exploration, and detailed study of hydrothermal vents. Beginning with clues provided by towed or lowered instruments, autonomous underwater vehicles (AUVs) can localize and make preliminary photographic surveys of vent fields. In addition to finding and photographing such sites, AUVs excel at providing regional context through fine-scale bathymetric and magnetic field mapping. Remotely operated vehicles (ROVs) enable close-up inspection, photomosaicking, and tasks involving manipulation of samples and instruments. Increasingly, ROVs are used to conduct in situ seafloor experiments. ROVs can also be used for fine-scale bathymetric mapping with excellent results, although AUVs are usually more efficient in such tasks

Mid-ocean ridge exploration with an autonomous underwater vehicle

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 4 (2007): 52-61.Human-occupied submersibles, towed vehicles, and tethered remotely operated vehicles (ROVs) have traditionally been used to study the deep seafloor. In recent years, however, autonomous underwater vehicles (AUVs) have begun to replace these other vehicles for mapping and survey missions. AUVs complement the capabilities of these pre-existing systems, offering superior mapping capabilities, improved logistics, and better utilization of the surface support vessel by allowing other tasks such as submersible operations, ROV work, CTD stations, or multibeam surveys to be performed while the AUV does its work. AUVs are particularly well suited to systematic preplanned surveys using sonars, in situ chemical sensors, and cameras in the rugged deep-sea terrain that has been the focus of numerous scientific expeditions (e.g., those to mid-ocean ridges and ocean margin settings). The Autonomous Benthic Explorer (ABE) is an example of an AUV that has been used for over 20 cruises sponsored by the National Science Foundation (NSF), the National Oceanic and Atmospheric Administration (NOAA) Office of Ocean Exploration (OE), and international and private sources. This paper summarizes NOAA OE-sponsored cruises made to date using ABE

Scientific challenges and present capabilities in underwater robotic vehicle design and navigation for oceanographic exploration under-ice.

This paper reviews the scientific motivation and challenges, development, and use of underwater robotic vehicles designed for use in ice-covered waters, with special attention paid to the navigation systems employed for under-ice deployments. Scientific needs for routine access under fixed and moving ice by underwater robotic vehicles are reviewed in the contexts of geology and geophysics, biology, sea ice and climate, ice shelves, and seafloor mapping. The challenges of under-ice vehicle design and navigation are summarized. The paper reviews all known under-ice robotic vehicles and their associated navigation systems, categorizing them by vehicle type (tethered, untethered, hybrid, and glider) and by the type of ice they were designed for (fixed glacial or sea ice and moving sea ice). © 2020 by the authors

Mid-water current aided localization for autonomous underwater vehicles

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Autonomous Robots 40 (2016): 1207–1227, doi:10.1007/s10514-016-9547-3.Survey-class Autonomous Underwater Vehi- cles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid- water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Cur- rent Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method dis- cussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Map- ping (SLAM) and on descents up to 3km deep with the Sentry AUV.This work is supported in part by NCRIS IMOS, the Australian Research Council (ARC), the New South Wales Government and the Woods Hole Oceanographic Institution.2017-02-1

Deep Sea Underwater Robotic Exploration in the Ice-Covered Arctic Ocean with AUVs

The Arctic seafloor remains one of the last unexplored areas on Earth. Exploration of this unique environment using standard remotely operated oceanographic tools has been obstructed by the dense Arctic ice cover. In the summer of 2007 the Arctic Gakkel Vents Expedition (AGAVE) was conducted with the express intention of understanding aspects of the marine biology, chemistry and geology associated with hydrothermal venting on the section of the mid-ocean ridge known as the Gakkel Ridge. Unlike previous research expeditions to the Arctic the focus was on high resolution imaging and sampling of the deep seafloor. To accomplish our goals we designed two new Autonomous Underwater Vehicles (AUVs) named Jaguar and Puma, which performed a total of nine dives at depths of up to 4062m. These AUVs were used in combination with a towed vehicle and a conventional CTD (conductivity, temperature and depth) program to characterize the seafloor. This paper describes the design decisions and operational changes required to ensure useful service, and facilitate deployment, operation, and recovery in the unique Arctic environment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86060/1/ckunz-17.pd

Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Robotics and Autonomous Systems 59 (2011): 988–1000, doi:10.1016/j.robot.2011.06.007.In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation [15] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 m x 2.1 m area) and in real time