An Improved Velocity Interval Estimation Method based on Zonotopic Kalman Filter for Autonomous Underwater Vehicles

TOUCH is a real-time computer vision system that has been developed in order to aid the operation of autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). The algorithm is based on a modified 4-connectivity approach that scans the incoming frames for a target hue range and based upon the largest connected blobs of hue, tracks an object and provides the two dimensional Cartesian coordinates of the desired object. Since few algorithms exist for tracking and recognizing objects in underwater environments, this algorithm provides a way for AUVs and ROVs to acquire, track or recognize objects by using a relatively fast and inexpensive vision system. This paper presents the details on TOUCH, its testing, some areas that warrant improvement and possible course of action for the future.

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

Optimal Sensor Placement for Multiple Underwater Target Localization with Acoustic Range Measurements

The paper is devoted to solving the problem of developing a synthesis method for combined position-force control systems (CS) for electric drives (ED) of multi-link underwater manipulators (MUM) mounted on autonomous underwater vehicles (AUV) operating in the mode of landing on the ground or on work sites, followed by rigid fixation of these AUV using special devices. To solve this problem, a comprehensive method is proposed in the first part of the paper. First, according to this method, a self-adjusting regulators are synthesized for the ED of each degree of freedom of the MUM, which makes it possible to stabilize its variable dynamic parameters at a given nominal level. This stabilization compensates for the influence of Coulomb and viscous friction, as well as part of the external torque caused by the mutual influences between the MUM links and their interaction with the viscous medium, on the quality of control. Then, to ensure the operation of the self-adjusting regulators and the ED CS, based on the initial nonlinear models of the ED, observers with a variable structure are synthesized to obtain information about the current values of external moments, as well as the rotation speeds and accelerations of all the ED output shafts. Using this information and measurements of only angle sensors of the specified output shafts, it was possible to synthesize position-force regulators for ED, minimizing the selected quadratic cost function, ensuring the specified movements of the output shafts of the item instance while creating the required moments. In the second part of the paper a synthesis of a combined position-force control system with all MUM elements was performed using these position-force regulators. As the results of computer modeling showed, this control system is capable of providing accurate movements of the MUM work tool along specified spatial trajectories and simultaneous creation of the required force effects on the work objects in the presence of the interaction of all its links with a viscous medium with variable parameters (viscous friction and added masses) while performing various complex technological operations.

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed.

Monitoring and M A Record-Breakin Geophysical Mon Copyright 2011 b 10.1029/2011GM Following destruction of the Deepwater Horizon drilling rig, while unmitigated blowout from the Macondo well was ongoing, NOAA scientific response cruise GU-10-02 (27 May to 4 June 2010) employed coordinated ship and autonomous underwater vehicle (AUV) operations to locate and study deep hydrocarbon plumes. The ship hydrocast survey localized maximum optical signals of a deep plume, centered at ~1150 m depth, approximately 13 km southwest of the blowout. Deployed at this location, the AUV conducted a high-resolution survey of plume structure, which indicated small-scale topographic influences on plume transport. Maximum plume intensity was observed along the western slope of Biloxi Dome. The orientation of gradients in plume intensity relative to isobaths indicated flow from the dome slope onto the dome top. In terms of the relative proportions of major hydrocarbon groups, all plume samples southwest of the blowout exhibited similar composition. The chemical composition of the plume southwest of the blowout was significantly different from the composition of a weaker deep plume observed southeast of the blowout. Variation in optical signal from a colored dissolved organic matter (CDOM) fluorometer (FCDOM) explained up to 97% (median 88%) of the variance in the concentrations of individual hydrocarbon compounds. AUV data also showed that FCDOM was highly correlated with three other optical measurements (r > 0.97) and oxygen measurements (r = 0.95). The results provide unique perspective on small-scale dynamics of a deep plume and illustrate the potential for studying subsurface plumes of dispersed oil using AUVs with off-the-shelf sensors.

Autonomous underwater vehicles (AUVs) are used to execute an increasingly challenging set of missions in commercial, environmental and defense industries. The resources available to the AUV in service of these missions are typically a limited power supply and onboard sensing of its local environment. Optimal path planning is needed to maximize the chances that these AUVs will successfully complete long-endurance missions within their power budget. A time-optimal path planner has been recently developed to minimize AUV mission time required to traverse a dynamic ocean environment at a specified speed through the water. For many missions, time minimization is appropriate because the AUVs operate at a fixed propeller speed. However, the ultimate limiting constraint on AUV operations is often the onboard power supply, rather than mission time. While an empirical or theoretical relationship between mission time and power could be applied to estimate power usage in the path planner, the real power usage and availability on an AUV varies mission-to-mission, as a result of multiple factors, including vehicle buoyancy, battery charge cycle, fin configuration, and water type or quality. In this work, we use data collected from two mid-size AUVs operating in various conditions to learn the mission-to-mission variability in the power budget so that it could be incorporated into the mission planner.

Path planning plays an extremely important role in the operation of Autonomous Underwater Vehicles (AUVs) to accomplish the underwater task safely, successfully and efficiently. This paper introduces the Fast Marching method for the path planning of AUVs in 3D large-scale battlefield environments to solve the problem that AUVs require low probability of being found, navigation security and low fuel consumption. The algorithm is able to find the optimal path between two waypoints in the work space and comprehensively takes factors such as the risk of collision with obstacles and mine, detection probability, sailing depth and path length into account. It also considers the maneuverability constraints of the AUV, including the safety depth and turning radius, to obtain the final flyable path. Finally, the proposed algorithm is tested in a large-scale area containing underwater terrain elevation map, sonar and mine density map to evaluate its feasibility, stability and efficiency.

Considering that few or no human operators are directly involved in the operation of Autonomous Marine Systems (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision-making. The current study identifies the criteria for an online risk model for AMS, which can be used to assess its validity and effectiveness.Taking an under-ice Autonomous Underwater Vehicle (AUV) operation as an example, the current work investigates how different risk analysis methods, namely the Preliminary Hazard Analysis (PHA), the Systems Theoretic Process Analysis (STPA), and Procedural Hazard and Operability Analysis (HAZOP), contribute to fulfilling the different criteria for online risk modeling of AMS. The analysis results show that STPA can be considered a good basis for developing an online risk model due to its relatively good coverage of the identified evaluation criteria, especially its ability to handle the interaction between system and software failure. In addition, considering some shortcomings of using STPA and the changing role of human operators in the AMS operation, PHA and Procedural HAZOP can be used as complementary tools. It is expected that the analysis results and conclusions can be adapted to other AMS as well.

Bathymetric navigation enables the long-term operation of autonomous underwater vehicles by reducing navigation drift errors with no need for GPS position fixes. In the case that a bathymetric map is not available, the simultaneous localization and mapping (SLAM) algorithm is required, but this increases computational complexity and memory requirement. Panel-based bathymetric SLAM could considerably reduce the computational burden. However, it may suffers from incorrect update when the vehicle does not belong to the updated panel. This study proposes a new update method, called weighted grid partitioning, which considers the probability distribution of a vehicle's location, and is more effective in terms of the map accuracy, computational burden, and memory usage compared to standard update methods. The feasibility of the proposed algorithm is verified through simulations.

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.KeywordsSensor NodeCommunication RangeInertial Measurement UnitRound Trip TimeAutonomous Underwater VehicleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

This paper proposes a new method for cruising-type autonomous underwater vehicles (AUVs) to track rough seafloors at low altitudes while also maintaining a high surge velocity. Low altitudes are required for visual observation of the seafloor. The operation of AUVs at low altitudes and high surge velocities permits rapid seafloor imaging over a wide area. This method works without high-grade sensors, such as inertial navigation systems (INS), Doppler velocity logs (DVL), or multi-beam sonars, and it can be implemented in lightweight AUVs. The seafloor position is estimated based on a reflection intensity map defined on a vertical plane, using measurements from scanning sonar and basic sensors of depth, attitude, and surge velocity. Then, based on the potential method, a reference pitch angle is generated that allows the AUV to follow the seafloor at a constant altitude. This method was implemented in the AUV HATTORI, and a series of sea experiments were carried out to evaluate its performance. HATTORI (Highly Agile Terrain Tracker for Ocean Research and Investigation) is a lightweight and low-cost testbed designed for rapid and efficient imaging of rugged seafloors, such as those containing coral reefs. The vehicle succeeded in following a rocky terrain at an altitude of approximately 2 m with a surge velocity of approximately 0.8 m/s. This paper also presents the results of sea trials conducted at Ishigaki Island in 2017, where the vehicle succeeded in surveying the irregular, coral-covered seafloor.

Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed.

Research on the influence of balance weight parameters on the motion performance of the seafloor mapping AUV in vertical plane