Abstract:To reduce human risk and maintenance costs, Autonomous Underwater Vehicles (AUVs) are involved in subsea inspections and measurements for a wide range of marine industries such as offshore wind farms and other underwater infrastructure. Most of these inspections may require levels of manoeuvrability similar to what can be achieved by tethered vehicles, called Remotely Operated Vehicles (ROVs). To extend AUV intervention time and perform closer inspection in constrained spaces, AUVs need to be more efficient and flexible by being able to undulate around physical constraints. A biomimetic fish-like AUV known as RoboFish has been designed to mimic propulsion techniques observed in nature to provide high thrust efficiency and agility to navigate its way autonomously around complex underwater structures. Building upon advances in acoustic communications, computer vision, electronics and autonomy technologies, RoboFish aims to provide a solution to such critical inspections. This paper introduces the first RoboFish prototype that comprises cost-effective 3D printed modules joined together with innovative magnetic coupling joints and a modular software framework. Initial testing shows that the preliminary working prototype is functional in terms of water-tightness, propulsion, body control and communication using acoustics, with visual localisation and mapping capability.Keywords: underwater robotics; biomimetic AUV; biomimetic propulsion; 3D seafloor reconstruction; acoustic communication
Early studies on propeller-driven AUV risk and reliability presented analysis methodologies with examples from a range of deployments (Podder et al. 2004; Griffiths and Trembanis 2007; Griffiths et al. 2009; Brito et al. 2010, 2012). More recently, two independent studies of underwater glider reliability have shown a significant difference in performance between gliders maintained and deployed by their developers and those deployed by purchasers (Brito et al. 2014). Rudnick et al. (2016) examined the operation of the Spray underwater glider by the development and operations team at the Scripps Institution of Oceanography. Their survival analysis concluded that for a 100-day mission, the probability of survival for the Spray glider was 0.83. For this calculation the authors considered the faults that led to premature mission abort albeit in some cases the mission was not aborted, because the main aim was to demonstrate a target mission length rather than to gather scientific data. In contrast, an analysis of commercially available gliders, operated by nondevelopers, concluded that the probability of a deep glider surviving a 90-day mission without premature mission abort was 0.5 (Brito et al. 2014). Differences in survival estimates have also been observed for the risk of vehicle loss, with Rudnick et al. (2016) reporting a survival of 0.95 for a 100-day mission, and Brito et al. (2014) reporting a survival of 0.8 for a 100-day mission. In their survival analysis, with respect to vehicle loss, Rudnick et al. (2016) considered faults that led to a loss of control over buoyancy and vehicle loss as failures. In the study by Brito et al. (2014), the authors considered vehicle loss as failures. Both studies argue that understanding and eliminating failure modes are key to increasing the probability of successful mission completion and survival.
Autonomous Underwater Vehicles: Dynamics, Developments and Risk Analysis
Tracking reliability growth is required in order to ensure effective outcomes from the deployment of autonomous systems. In this paper we present Bayesian formalism for tracking the reliability growth of autonomous underwater vehicles. Each mission was considered as a test in a binomial trial. We applied the method to update the risk of the ISE Explorer AUV, following the Arctic campaign in 2010.
The work done by Naik et al. in 2007 [22] began with the state-dependent Riccati equation technique that provides an effective means of designing non-linear control systems for minimum and non-minimum autonomous phases of UV models. This technique was used to develop a non-linear control model for autonomous UVs. To conclude controlling systems for UV, Shi et al. worked on a literature review on maritime mechatronic systems [23] that included some results in marine control, like developments in terms of control system designs for surface vessels, underwater robotic vehicles, profiling floats, underwater gliders, wave energy converters, and offshore wind turbines.
Interdisciplinary teams can use MATLAB and Simulink as a common integration environment throughout the entire autonomous underwater vehicle workflow. From systems engineering to platform modeling, environment simulation, and autonomy algorithm design, Model-Based Design helps you reduce risk and build confidence in system performance well in advance of the sea trial.
Past and current projects on which Dr. Omidvar has collaborated include bridge scour monitoring and risk assessment using geographic information system (GIS) and autonomous underwater vehicles (AUV), prediction of depth of burial of unexploded ordnances (UxO) at formerly used defense sites (FUDs), shape optimization of 3D-printed discrete reinforcements in sand, micromechanical response of sand to pile driving and jacking, study of the fundamental physics of projectile penetration in soils, dynamic analysis of earth dams and reinforced soil slopes, torpedo anchors with offshore applications, and approximate analytical solutions to high order nonlinear partial differential equations.
Tzu Yang then went on to earn his M.Sc (Safety, Health, and Environmental Technology) from the National University of Singapore and Certified Industrial Hygienist (CIH) certification from the Board for Global EHS credentialing (BGC), formally known as the American Board of Industrial Hygiene (ABIH). Tzu Yang completed his Ph.D. in Maritime Engineering from the University of Tasmania (Australia) focusing on risk analysis of autonomous underwater vehicle operations in the Antarctic. Much of his research examines different risk analysis methodologies used across various disciplines and industries. During his research, he published several papers in journals such as the Journal of Risk Analysis and Journal of Advanced Computational Intelligence and Intelligent Informatics.
Fully-autonomous operation of USVs is always limited due to the loss risks in the present applications. Instead, the semi-autonomy mode, such as teleoperation by the crew onshore, has been favored over the fully-autonomy in the past years. As a sustained observing platform (Wave Glider), the USVs provide opportunities to complement fixed moored buoys, underwater vehicles, Lagrangian drifters, and some seafloor observatories. Meanwhile coordinated groups of USVs could be directed to intensively study real-time conditions in the ocean with a lower cost compared to mooring arrays. Future development of USVs tends to utilize artificial intelligence technology and high-level sensors to extend the capabilities in more complicated missions without human intervention. It should be noted that the use of USVs must be ready to interact with all manner of shipping, that is, the on-going research programs should address the technical questions posed by the intersection of USVs and the nautical rules of the road [48].
SCUBA diving activities are classified as high-risk due to thedangerous environment, dependency on technical equipment that ensures lifesupport, reduced underwater navigation and communication capabilities all ofwhich compromise diver safety. While autonomous underwater vehicles (AUVs)have become irreplaceable tools for seabed exploration, monitoring, andmapping in various applications, they still lack the higher cognitivecapabilities offered by a human diver. The research presented in this paperwas carried out under the EU FP7 "CADDY--Cognitive Autonomous DivingBuddy". It aims to take advantage of both human diver and AUVcomplementary traits by making their synergy a potential solution formitigation of state of the art diving challenges. The AUV increases diversafety by constantly observing the diver, provides navigation aiding bydirecting the diver and offers assistance (e.g., lights, tool fetching,etc.). The control algorithms proposed in the paper provide a foundation forimplementing these services. These algorithms use measurements fromstereo-camera, sonar and ultra-short baseline acoustic localization to ensurethe vehicle constantly follows and observes the diver. Additionally, thevehicle maintains a relative formation with the diver to allow observationfrom multiple viewpoints and to aid underwater navigation by pointing towardsthe next point of interest. Performance of the proposed algorithms isevaluated using results from pool experiments.
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