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This paper describes the design and implemen-tation of a model-based sonar servoing control scheme for Autonomous Underwater Vehicles (AUVs). The proposed con-troller is designed for autonomous surveillance of underwater structures and it is robust against external disturbances and parametric uncertainties in the AUV dynamic model. The sen-sor suit...
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... vehicle used in this work is the Nessie VI AUV (Fig. 1), which is modeled as a rigid body subject to external forces and torques. Let {I} be an inertial coordinate frame on the wall to be inspected with the x and z axis showing inwards and downwards respectively, and {B} a body-fixed coordinate frame whose origin O B is located in front of the vehicle at the sonar sensor (see Fig. 2). T be ...
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... vehicle used in this work is the Nessie VI AUV ( Fig. 1), which is modeled as a rigid body subject to external forces and torques. Let { I } be an inertial coordinate frame on the wall to be inspected with the x and z axis showing inwards and downwards respectively, and { B } a body-fixed coordinate frame whose origin O B is located in front of the vehicle at the sonar sensor (see Fig. 2). Furthermore, let η 1 = [ x, y, z ] T be the position and η 2 = [ φ, θ, ψ ] T be the orientation of O B in { I } with φ, θ, ψ denoting the roll, pitch and yaw angles. Let v 1 = [ u, v, w ] T be the linear velocity (i.e., the longitudinal (surge), transverse (sway) and vertical (heave)) of O B with respect to { I } expressed in { B } and v = [ p, q, r ] T be the angular velocity (roll, pitch, yaw) around the longitudinal, transverse and vertical axis respectively. Hence, the kinematic equations of motion can be written ...
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Citations
... After applying a standard Hough transform combined with an improved sonar model to detect line features, a wall is tracked by an Extended Kalman Filter (EKF). A method to detect the wall and estimate the pose of the vehicle is proposed using the Random Sample Consensus (RANSAC) [10] using measurements received from a multibeam imaging sonar [11]. Recently, an approach using reinforcement learning was studied in [12], where a set of ranging sensor is used and efficiently manipulated to allow an underwater vehicle to navigate along the wall. ...
Hull inspection is an important task to ensure sustainability of ships. To overcome the challenges of hull structure inspection in an underwater environment in an efficient way, an autonomous system for hull inspection has to be developed. In this paper, a new approach to underwater ship hull inspection is proposed. It aims at developing the basis for an end-to-end autonomous solution. The real-time aspect is an important part of this work, as it allows the operators and inspectors to receive feedback about the inspection as it happens. A reference mission plan is generated and adapted online based on the inspection findings. This is done through the processing of a multibeam forward looking sonar to estimate the pose of the hull relative to the drone. An inspection map is incrementally built in a novel way, incorporating uncertainty estimates to better represent the inspection state, quality, and observation confidence. The proposed methods are experimentally tested in real-time on real ships and demonstrate the applicability to quickly understand what has been done during the inspection.
... Nowadays, water conveyance tunnels have been an important transportation route in hydraulic engineering. 1 When detecting the wall crack in the water conveyance tunnels, the ability of following the wall at a certain distance is the basic premise of autonomous underwater vehicle's (AUV's) safe operation. 2 Ranging sonar is widely used in obtaining the distance of obstacles, its principle is the transducer actively emits acoustic waves and obtains the distance information of obstacle by receiving the echo reflected by the obstacle. 3 Efficient access to stable and accurate distance data is an important performance index of the ranging sonar. ...
When autonomous underwater vehicle following the wall, a common problem is interference between sonars equipped in the autonomous underwater vehicle. A novel work mode with weighted polling (which can be also called “weighted round robin mode”) which can independently identify the environment, dynamically establish the environmental model, and switch the operating frequency of the sonar is proposed in this article. The dynamic weighted polling mode solves the problem of sonar interference. By dynamically switching the operating frequency of the sonar, the efficiency of following the wall is improved. Through the interpolation algorithm based on velocity interpolation, the data of different frequency ranging sonar are time registered to solve the asynchronous problem of multi-sonar and the system outputs according to the frequency of high-frequency sonar. With the reinforcement learning algorithm, autonomous underwater vehicle can follow the wall at a certain distance according to the distance obtained from the polling mode. At last, the tank test verified the effectiveness of the algorithm.
... This aspect, while solving elegantly the problem of saturation, introduces some restrictions in the design of customized control architectures. An example of this is seen in Karras et al. (2013Karras et al. ( , 2014 where a common control schema has been adapted to a specific operating mode that an AUV may encounter during its long-term operations. In such cases the fault adaptation should be attempted even if in presence of control strategies, selected according to the current task. ...
Energy awareness and fault tolerance are important aspects for extending the autonomy levels of today’s autonomous vehicles. With the aim of preparing the way for persistent autonomous operations of underwater vehicles this work focusses its efforts on investigating the effects of actuator failures, on an autonomous underwater vehicle (AUV) capable of long-term inspection missions. This paper introduces an energy-aware architecture that by observing the use of the on-board resources is capable of detecting faults and monitors the performance of the thruster subsystem in modern AUVs. The effect is an increased autonomy level in presence of unexpected events like performance degradations or sudden failures. Moreover an important contribution of this work is to process the great volume of information, collected at the lower sensor levels, into operational parameters that can be treated by higher level modules. These parameters form part of an abstract representation of concepts and capabilities that are available at a given time during the mission’s execution. Once this representation has been updated it is made available to the planning and execution components that can adapt the mission’s behaviour using the most recent knowledge about the vehicle’s state. To validate the proposed approach we evaluate our system on a real platform, Nessie VIII AUV, in both in real sea conditions and in a controlled test tank.
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When detecting the wall crack, interferences caused by wall effect will significantly increase the difficulty of controlling AUV. This paper focuses on observing and compensating for interferences in the design of the controller. The optimal weight distribution algorithm is proposed to obtain the desired real-time optimal heading, which ensures that the AUV can achieve autonomous following of the wall by adjusting the heading. The backstepping sliding mode controller based on a slow time-varying adaptive disturbance observer (ADO) is proposed, which can effectively observe and compensate for the interference during AUV near-wall following. Results show that the tracking error of the proposed controller can be greatly reduced compared with the PID controller, and the tracking error is significantly reduced compared with the conventional sliding mode controller. The proposed controller can converge fast with small overshoot and stable control effect. The ADO provides strong robustness against interference, such as wall effect. Thus, AUVs can stably perform near-wall-following tasks and capture clear videos.
We present a seabed profile estimation and following method for close proximity inspection of 3D underwater structures
using autonomous underwater vehicles (AUVs). The presented method is used to determine a path allowing the
AUV to pass its sensors over all points of the target structure, which is known as coverage path planning. Our profile
following method goes beyond traditional seabed following at a safe altitude and exploits hovering capabilities of recent
AUV developments. A range sonar is used to incrementally construct a local probabilistic map representation of
the environment and estimates of the local profile are obtained via linear regression. Two behavior-based controllers
use these estimates to perform horizontal and vertical profile following. We build upon these tools to address coverage
path planning for 3D underwater structures using a (potentially inaccurate) prior map and following cross-section
profiles of the target structure. The feasibility of the proposed method is demonstrated using the GIRONA 500 AUV
both in simulation using synthetic and real-world bathymetric data and in pool trials.
