MERLIN - A decade of large AUV experience at Memorial University of Newfoundland

Lewis, Ron; Bose, Neil; Lewis, Sara; King, Peter; Walker, Dan; Devillers, Rodolphe; Ridgley, Nick; Husain, Tahir; Munroe, James; Vardy, Andrew

doi:10.1109/auv.2016.7778675

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Upon starting a mission, the waypoint-generator function generated randomly spaced waypoints. The number of waypoints was automatically adjusted to the size of the operational area using Equation (8). With a given operational area of 1 km 2 and an assumed plume size of 0.16 km 2 , five waypoints were assigned.…”

Section: Waypoints Generator and Tsp Solvermentioning

confidence: 99%

“…, where n = number of waypoints to be generated op.area = AUV operation area s = predicted plume size (8) When the waypoints for a robotic agent to visit are distributed in a large search space, an efficient search procedure is required, regardless of robustness [13]. The optimum path in this context means the calculated route that requires the minimum total travel distance within the given area.…”

Section: Waypoints Generator and Tsp Solvermentioning

confidence: 99%

“…We have developed an adaptive sampling system for the Memorial University of Newfoundland (MUN) Explorer AUV to autonomously delineate an oil plume. The Memorial University Explorer AUV has been described in previous work (e.g., [7,8]). The algorithm simulated the following tasks: waypoint assignment, AUV trajectory generation, and robot model establishment.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle

Hwang

Bose

Nguyen

et al. 2020

JMSE

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We introduce an adaptive sampling method that has been developed to support the Backseat Driver control architecture of the Memorial University of Newfoundland (MUN) Explorer autonomous underwater vehicle (AUV). The design is based on an acoustic detection and in-situ analysis program that allows an AUV to perform automatic detection and autonomous tracking of an oil plume. The method contains acoustic image acquisition, autonomous triggering, and thresholding in the search stage. A new biomimetic search pattern, the bumblebee flight path, was designed to maximize the spatial coverage in the oil plume detection phase. The effectiveness of the developed algorithm was validated through simulations using a two-dimensional planar plume model and a 90-degree scanning sensor model. The results demonstrate that the bumblebee search design combined with a genetic solution for the Traveling Salesperson Problem outperformed a conventional lawnmower survey, reducing the AUV travel distance by up to 75.3%. Our plume detection strategy, using acoustic sensing, provided data of plume location, distribution, and density, over a sector in contrast with traditional chemical oil sensors that only provide readings at a point.

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Section: Waypoints Generator and Tsp Solvermentioning

confidence: 99%

Section: Waypoints Generator and Tsp Solvermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle

Hwang

Bose

Nguyen

et al. 2020

JMSE

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“…Development of this system was for Memorial University of Newfoundland's (MUN) Explorer AUV, shown in Figure 1. The MUN Explorer is a 4.5m long, 3000m depth rated vehicle manufactured by International Submarine Engineering (ISE) (ISE, 2016;Lewis et al, 2016). This survey class AUV can traverse distances over 100km and navigates using a true-North sensing fibre optic gyroscope and a Doppler Velocity Log (DVL), capable of providing velocity relative to the seabed at altitudes less than 200m.…”

Section: Target Platformmentioning

confidence: 99%

Teach‐and‐repeat path following for an autonomous underwater vehicle

King

Vardy

Forrest

2018

Journal of Field Robotics

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This paper presents a teach-and-repeat path following method for an Autonomous Underwater Vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then re-navigate along this path, either to return to the start location, or to repeat the route. Utilizing unique assumptions about the sonar image generation process, this system exhibits robust image matching capabilities, providing observations to a discrete Bayesian filter which maintains an estimate of progress along the path. Image matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1km and 5km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path following performance was as desired, with the AUV maintaining close offset to the path.

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“…These vehicles generally have a single rear thruster and a set of control surfaces. A wide range of such platforms exist, from a small scale such as the Bluefin SandShark ( Naglak et al, 2018 ) to the large scale MERLIN ( Lewis et al, 2016 ). The majority of AUVs are optimized for operational runtimes on the order of 12–24 h with manual charging and re-tasking required between missions ( Page and Mahmoudian, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Underwater Docking Approach and Homing to Enable Persistent Operation

et al. 2021

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One of the main limiting factors in deployment of marine robots is the issue of energy sustainability. This is particularly challenging for traditional propeller-driven autonomous underwater vehicles which operate using energy intensive thrusters. One emerging technology to enable persistent performance is the use of autonomous recharging and retasking through underwater docking stations. This paper presents an integrated navigational algorithm to facilitate reliable underwater docking of autonomous underwater vehicles. Specifically, the algorithm dynamically re-plans Dubins paths to create an efficient trajectory from the current vehicle position through approach into terminal homing. The path is followed using integral line of sight control until handoff to the terminal homing method. A light tracking algorithm drives the vehicle from the handoff location into the dock. In experimental testing using an Oceanserver Iver3 and Bluefin SandShark, the approach phase reached the target handoff within 2 m in 48 of 48 tests. The terminal homing phase was capable of handling up to 5 m offsets with approximately 70% accuracy (12 of 17 tests). In the event of failed docking, a Dubins path is generated to efficiently drive the vehicle to re-attempt docking. The vehicle should be able to successfully dock in the majority of foreseeable scenarios when re-attempts are considered. This method, when combined with recent work on docking station design, intelligent cooperative path planning, underwater communication, and underwater power transfer, will enable true persistent undersea operation in the extremely dynamic ocean environment.

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MERLIN - A decade of large AUV experience at Memorial University of Newfoundland

Cited by 7 publications

References 21 publications

Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle

Acoustic Search and Detection of Oil Plumes Using an Autonomous Underwater Vehicle

Teach‐and‐repeat path following for an autonomous underwater vehicle

Underwater Docking Approach and Homing to Enable Persistent Operation

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