Position Of Autonomous Underwater Vehicle Research Articles

AUV-Based Plume Tracking: A Simulation Study

This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

Moving Horizon Estimation for Moving Long Baseline based on Linear Positioning Model

This paper presents the real-time moving horizon estimation (MHE) for Moving Long Baseline (MLBL) positioning system. By establishing the linear positioning model, we formulate MLBL positioning problem in a linear, time-invariant, discrete-time system. In this system, we assume that the velocity of Autonomous Underwater Vehicle (AUV) is known, and the distances between Unmanned Surface Vessels (USVs) and AUV are the measurements. Then, we design a moving horizon estimator with the dynamic model and state constrains are considered. This estimator determines the position of AUV by solving a constrained optimization problem. The estimated positions are computed by minimizing the cost function. Simulation results demonstrate the performance of the MHE algorithm with the comparison of Kalman Filter (KF).

Underwater terrain positioning method based on least squares estimation for AUV

To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwater terrainmatching data. An underwater terrain interpolation error compensation method based on fractional Brownian motion is proposed for defects of normal terrain interpolation, and an underwater terrain-matching positioning method based on least squares estimation (LSE) is proposed for correlation analysis of topographic features. The Fisher method is introduced as a secondary criterion for pseudo localization appearing in a topographic features flat area, effectively reducing the impact of pseudo positioning points on matching accuracy and improving the positioning accuracy of terrain flat areas. Simulation experiments based on electronic chart and multi-beam sea trial data show that drift errors of an inertial navigation system can be corrected effectively using the proposed method. The positioning accuracy and practicality are high, satisfying the requirement of underwater accurate positioning.

AUV docking experiments based on vision positioning using two cameras

An AUV(Autonomous Underwater Vehicle) docking test got finished successfully in the general deep water pool lab of Harbin Engineering University. Two major problems got solved. One is reliable underwater navigation method providing accurate relative position information, which is basically needed while AUV approaching. Four green LED lights are fixed around the station and two cameras are installed in AUV’s head part. A reliable vision positioning method using two cameras is adopted. Core algorithm switches between combining monocular and binocular positioning method according to the number of image in both cameras.. The other problem is that control system should be designed suited to the docking procedure, that is to say, outputs of controller ensures the declining of deviation between AUV position and center line of dock station. A two-layer control system is adopted considering the underactuated feature of our test bed. The guidance controller is designed based on Lyapunov stability theory. It outputs reference heading and trimming angle according to position deviations in horizontal and vertical plane respectively. Control parameters are adaptively adjusted by fuzzy algorithm. The execution controller eliminates errors of attitude angle employing an adaptively S-surface non-linear function. Pool test demonstrates that the navigating and control system works well. The system guarantees successful docking rate greater than 80%.

Distributed estimation of sensors position in underwater wireless sensor network

ABSTRACTIn this paper, a localisation method for determining the position of fixed sensor nodes in an underwater wireless sensor network (UWSN) is introduced. In this simple and range-free scheme, the node localisation is achieved by utilising an autonomous underwater vehicle (AUV) that transverses through the network deployment area, and that periodically emits a message block via four directional acoustic beams. A message block contains the actual known AUV position as well as a directional dependent marker that allows a node to identify the respective transmit beam. The beams form a fixed angle with the AUV body. If a node passively receives message blocks, it could calculate the arithmetic mean of the coordinates existing in each messages sequence, to find coordinates at two different time instants via two different successive beams. The node position can be derived from the two computed positions of the AUV. The major advantage of the proposed localisation algorithm is that it is silent, which leads to energy efficiency for sensor nodes. The proposed method does not require any synchronisation among the nodes owing to being silent. Simulation results, using MATLAB, demonstrated that the proposed method had better performance than other similar AUV-based localisation methods in terms of the rates of well-localised sensor nodes and positional root mean square error.

Distributed formation control of 6-DOF autonomous underwater vehicles networked by sampled-data information under directed topology

In this paper, a sampled-data networking strategy is investigated for the formation problem of multiple 6-DOF autonomous underwater vehicles (AUVs) in the presence of external disturbances. Unlike the common condition in most literatures associated with multiple vehicles, where the information is assumed to be exchanged continuously, in our proposed strategy, the position and orientation information of the multiple AUVs is exchanged according to a discrete-time sequence, which is more applicable to current technologies for underwater communications. More precisely, this strategy can be modeled by a sample-and-hold mechanism and is capable of both uniform and non-uniform sampling cases. In particular, it has a potential advantage from an energy perspective, since communication reduction always leads to less energy consumption. By model transformation, distributed controllers are designed based on input-to-state stability (ISS) property to ensure that both configuration formation and rendezvous-type formation can be achieved. An optimization procedure is also presented to calculate the maximum allowable sampling period. Finally, a numerical example is provided to illustrate the effectiveness and applicability of the theoretical results.

Hybrid Baseline Localization for Autonomous Underwater Vehicles

This paper presents a hybrid baseline (HBL) navigation method for autonomous underwater vehicles (AUVs). In this approach, a floating acoustic transponder is used to augment moving short baseline (MSBL) navigation, in which two transponders are mounted on a source ship. Kinematic information regarding the floating buoy and source ship are communicated via acoustic messages to the AUV fleet. Simultaneously, clock synchronization and a predefined broadcast cycle enables transponder-to-AUV range estimates based on the acoustic message time-of-flight. Using the extended Kalman (EKF) filter formulation, the broadcasted kinematic information and acoustic ranges are combined onboard each AUV in the fleet to estimate the states of the source ship, the floating transponder, and the AUV itself. Simulation was used to optimize the configuration and performance of the HBL navigation, and field tests were performed to evaluate the navigational performance. In post-processing, high-accuracy position and timing data for the source ship and floating buoy was added to the onboard navigation data in an EKF to recreate the path of the AUV. The accuracy of this post-mission localization was evaluated relative to AUV positions independently measured by an acoustic tracking array at the test facility. The resulting AUV position estimation showed a mean error of 1.59 m, an improvement over the previous result (using MSBL navigation only) of 3.30 m.

Comparing Autonomous Underwater Vehicle (AUV) and Vessel-based Tracking Performance for Locating Acoustically Tagged Fish

Autonomous underwater vehicles (AUV’s) are increasingly used to collect physical, chemical, and biological information in the marine environment. Recent efforts include merging AUV technology with acoustic telemetry to provide information on the distribution and movements of marine fish. We compared surface vessel and AUV tracking capabilities under rigorous conditions in coastal waters near Juneau, Alaska. Tracking surveys were conducted with a REMUS 100 AUV equipped with an integrated acoustic receiver and hydrophone. The AUV was programmed to navigate along predetermined routes to detect both reference transmitters at 20–500 m depths and tagged fish and crabs in situ. Comparable boat surveys were also conducted. Transmitter depth had a major impact on tracking performance. The AUV was equally effective or better than the boat at detecting reference transmitters in shallow water, and significantly better for transmitters at deeper depths. Similar results were observed for tagged animals. Red king crab, Paralithodes camtschaticus, at moderate depths were recorded by both tracking methods, while only the AUV detected Sablefish, Anoplopoma fimbria, at depths exceeding 500 m. Strong currents and deep depths caused problems with AUV navigation, position estimation, and operational performance, but reflect problems encountered by other AUV applications that will likely diminish with future advances, enhanced methods, and increased use.

A State Observation Technique for Highly Compressed Source Coding of Autonomous Underwater Vehicle Position

In this paper, a novel technique is presented for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. In this work, both the sending vehicle and receiving vehicle or human operator are equipped with a shared real-time simulation of the sender's state based on the prior transmitted positions. Thus, only the innovation between the sender's actual state and the shared state need be sent over the link, such as a very low throughput acoustic modem. The distribution of this innovation can be modeled a priori or assembled adaptively. This distribution is then paired with an arithmetic entropy encoder, producing a very low cost representation of the vehicle's position vector. This system was analyzed on experimental data from the GLINT10 and AGAVE07 expeditions involving two different classes of AUVs performing a diverse number of maneuvers, and implemented on a fielded vehicle in the MBAT12 experiment. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-b integer baseline, was achieved.

A Neurodynamics Control Strategy for Real-Time Tracking Control of Autonomous Underwater Vehicles

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity.

Underwater Terrain Matching Positioning Method Based on MLE for AUV

Aiming at the precise positioning of autonomous underwater vehicle(AUV) during submerged operation for long periods of time,an underwater terrain matching positioning method based on maximal likelihood estimation(MLE) is proposed.In this method,multibeam echo sounder(MBES) is taken as the underwater terrain measuring means,and maximum likelihood algorithm is used for correlation analysis of terrain character.For the influence from false peaks of likelihood function at flat bottom area,the Fisher criterion is introduced to restrict it.The false peaks are eliminated effectively and the discrimination of flat bottom area is enhanced.The simulation results based on one electronic chart data and sea test data from a multibeam echo sounder show that the proposed method is effective and feasible.

Acoustic Guidance for Gavia Inertial Navigation System

Several options exist for acoustically aiding the Gavia AUV navigation system. Currently a Teledyne Benthos LBL and DAT systems are in the process of being integrated on to the AUV. The currently available PinPoint LBL system from LinkQuest has been used to improve positional accuracy of the Gavia Autonomous Underwater Vehicle (AUV) navigation system. A separate Kalman filter is used to estimate the AUV position using the position from the inertial system as an a priori estimate. Feeding the acoustically obtained position back into the inertial navigation system improves the overall performance of the system. This method is robust for failures in range measurements.

An In-situ Correction Method of Position Error for an Autonomous Underwater Vehicle Surveying the Sea Floor

This paper presents an in-situ correction method to compensate for the position error of an autonomous underwater vehicle (AUV) near the sea floor. AUVs generally have an inertial navigation system assisted with auxiliary navigational sensors. Since the inertial navigation system shows drift in position without the bottom reflection of a Doppler velocity log, external acoustic positioning systems, such as an ultra short baseline (USBL), are needed to set the position without surfacing the AUV. The main concept of the correction method is as follows: when the AUV arrives near the sea floor, the vehicle moves around horizontally in a circular mode, while the USBL transceiver installed on a surface vessel measures the AUV's position. After acquiring one data set, a least-square curve fitting method is adopted to find the center of the AUV's circular motion, which is transferred to the AUV via an acoustic telemetry modem (ATM). The proposed method is robust for the outlier of USBL, and it is independent of the time delay for the data transfer of the USBL position with the ATM. The proposed method also reduces the intrinsic position error of the USBL, and is applicable to the in-situ calibration as well as the initialization of the AUVs' position. Monte Carlo simulation was conducted to verify the effectiveness of the method.

Convex Optimization Algorithms for Cooperative Localization in Autonomous Underwater Vehicles

In this paper, a cooperative localization algorithm for autonomous underwater vehicles (AUVs) is proposed. A “parallel” model is adopted to describe the cooperative localization problem instead of the traditional “leader-follower” model, and a linear programming associated with convex optimization method is used to deal with the problem. After an unknown-but-bounded model for sensor noise is assumed, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements. Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space. Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs. Simulation results are presented for a typical localization example of the AUV formation. The results show that our positioning method offers a good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, and this validates that it is an economical and practical positioning approach compared with the traditional approach.

Underwater SLAM in man‐made structured environments

AbstractThis paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas, and marine platforms. A mechanically scanned imaging sonar is used to obtain information about the location of vertical planar structures present in such environments. A robust voting algorithm has been developed to extract line features, together with their uncertainty, from the continuous sonar data flow. The obtained information is incorporated into a feature‐based simultaneous localization and mapping (SLAM) algorithm running an extended Kalman filter. Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Moreover, a procedure to build and maintain a sequence of local maps and to posteriorly recover the full global map has been adapted for the application presented. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. © 2008 Wiley Periodicals, Inc.

Dynamic positioning and way-point tracking of underactuated AUVs in the presence of ocean currents

This paper addresses the problem of dynamic positioning and way-point tracking of underactuated autonomous underwater vehicles (AUVs) in the presence of constant unknown ocean currents and parametric modelling uncertainty. A non-linear adaptive controller is proposed that steers an AUV along a sequence of way-points consisting of desired positions (x, y) in a inertial reference frame, followed by vehicle positioning at the final target point. The controller is first derived at the kinematic level assuming that the ocean current disturbance is known. An exponential observer for the current is then designed and convergence of the resulting closed-loop system trajectories is analysed. Finally, integrator backstepping and Lyapunov based techniques are used to extend the kinematic controller to the dynamic case and to deal with model parameter uncertainty. Simulation results with a dynamic model of an underactuated autonomous underwater shuttle for the transport of benthic labs are presented and discussed.

Moth-inspired chemical plume tracing on an autonomous underwater vehicle

This paper presents a behavior-based adaptive mission planner (AMP)to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV)equipped with multiple types of sensors that measure chemical concentration,the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume,tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying up wind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants.

Terrain Navigation for Underwater Vehicles Using the Correlator Method

In many autonomous underwater vehicle (AUV) applications of long duration, there is a need to accurately determine the AUV's position in an effective way, in terms of both cost and energy consumption. Such situations may occur in reconnaissance operations, mine counter measure operations, and when an AUV is sampling the characteristics of the environment along a track. Since underwater maps are rare, there also is a need for navigational methods that can accommodate very infrequent sampling in terms of time and space. This work describes a new method to solve the above problem by using a multibeam sonar for three-dimensional sampling of the bottom topography and a linear Kalman filter in a nearly optimal way. The proposed method is very accurate, robust, and computationally efficient compared to other methods that are used for terrain navigation. This method is also suitable for continuous navigation.

A fuzzy Kalman filter optimized using a multi-objective genetic algorithm for enhanced autonomous underwater vehicle navigation

In an autonomous underwater vehicle integrated navigation system, short-term temporal accuracy is provided by an inertial navigation subsystem (INS) and long-term accuracy by a global positioning system (GPS). The Kalman filter has been a popular method for integrating the data produced by the two systems to provide optimal estimates of autonomous underwater vehicle position and attitude. In this paper, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the latter where integration with GPS data takes place. The use of a fuzzy-rule-based adaptation scheme to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for an adaptation scheme is first carried out using a heuristic approach. Multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Simulation results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation system.

A fuzzy Kalman filter optimized using a multi-objective genetic algorithm for enhanced autonomous underwater vehicle navigation

In an autonomous underwater vehicle integrated navigation system, short-term temporal accuracy is provided by an inertial navigation subsystem (INS) and long-term accuracy by a global positioning system (GPS). The Kalman filter has been a popular method for integrating the data produced by the two systems to provide optimal estimates of autonomous underwater vehicle position and attitude. In this paper, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the latter where integration with GPS data takes place. The use of a fuzzy-rule-based adaptation scheme to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for an adaptation scheme is first carried out using a heuristic approach. Multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Simulation results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation system.