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The exploration of the underwater environments has recently accelerated with the development of the Autonomous Underwater Vehicle (AUV). One of the key elements for enhancing the autonomy of AUVs navigation across various applications is efficient path planning. Reinforcement Learning (RL) methods have been successfully introduced for path planning of AUVs, particularly in high-dimensional state spaces, where prior knowledge of the environment is unfeasible. In this work, we propose a Deep Reinforcement Learning (DRL) method for efficient AUV navigation in 3 Dimension (3D) environments, utilizing input from vision sensors to obtain information about the motion of the AUV and the surrounding space. We adopt a multi-tier approach in order to validate the performance of the proposed DRL approach in three different neural network architectures leveraging on adaptation and accuracy, with path length, execution time and success of operation being considered as the optimization objectives. Finally, a simulation platform is built to evaluate the performance of the proposed method, with experimental results showcasing enhanced decision-making capability for the AUV navigation, which translates to a higher level of autonomy for the vehicle in unknown environments.

In this study, a backstepping sliding mode control method (NDO-ABSC) based on nonlinear disturbance observer (NDO) and adaptive line-of-sight guidance (ALOS) is proposed to address the three-dimensional path following control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of ocean currents, unmodeled dynamics, and other unknown disturbances. First, the following error equations in the current environment are established in the Serret-Frenet coordinate system. Then, ALOS is designed to estimate the variations changes in angle-of-attack and crab angle caused by time-varying currents. In the kinematic controller, a parameter adaptive law, a control law for the virtual target point, and a virtual desired angular velocity are designed by backstepping. Subsequently, the composite uncertainty disturbance is observed and compensated by constructing a nonlinear disturbance observer, and a dynamic controller is designed by backstepping method, and a sliding mode control term is introduced in the control law to enhance the robustness of the system to the uncertainty disturbance. Finally, the good performance and strong robustness of the proposed method in 3D path following control are verified by numerical simulation.

The manuscript introduces a radio wave-based wireless communication system tailored for Autonomous Underwater Vehicles (AUVs), optimized for underwater deployment. Initial experiments to ascertain the fundamental properties of a loop antenna were conducted in a small-scale water tank, followed by comprehensive communication and video transmission trials in a larger tank with an AUV. A specialized waterproof enclosure was developed to house a waterproof antenna, a Vector Network Analyzer (VNA), and a communication board. This setup facilitated the evaluation of the loop antenna’s radio propagation attributes and the identification of frequencies conducive to underwater communication. Designed with practical deployment in mind, the system incorporates a 2-meter loop antenna for signal transmission and a 0.5m by 0.8m rectangular loop antenna integrated into the AUV for signal reception. Utilizing a wavelet Orthogonal Frequency Division Multiplexing (OFDM) communication board and tuning to lower frequencies, the system successfully established User Datagram Protocol (UDP) and Transfer Control Protocol (TCP) communications within a few meter radius from the transmitting antenna, achieving data transfer rates up to 6.8 Mbps.

The present paper presents a fault-tolerant control for an AUV in the presence of a critical failure in the actuators that may require an emergency operation to recover it or to drive it to a safe point. In this context, the control scheme proposed deals with a single thruster in operational conditions to command the vehicle towards a desired direction and reach a safe target point. In addition, the AUV is commanded with only two control actions on the available thruster, driving the vehicle through the desired direction following a spiral-like path and keeping it within the neighbourhood of the target point. The fault-tolerant control proposed is simple and robust enough to be applied to multiple kinds of AUVs without the need of accurate parameter design. The stability and well performance of the control scheme proposed is analytically demonstrated, and simulation examples illustrate the key results derived.

In order to improve the persistent operation capacity of autonomous underwater vehicle (AUV), autonomous recovery system draws more and more attention in recent years. A hybrid scheme combined with navigation, guidance and control is proposed to complete the function of autonomous recovery of AUV in the paper. Based on the hybrid scheme, the autonomous recovery is firstly transformed into the trajectory tracking problem, and the Dubins path planning method is adopted to build the three-dimensional(3D) motion path of AUV. Secondly, the virtual target guidance strategy is introduced to guide the AUV to track the desired path. Finally, a non-singular terminal sliding mode controller is designed to control the AUV. Due to the existence of model inaccuracy of AUV and external disturbances from the environment, a finite-time disturbance observer is adopted to estimate the disturbance and uncertainty in the system. Numerical simulation is conducted to verify its performance. The simulation results show that a 3D motion path can be planed and the AUV can track the motion path well.

Designing a reasonable obstacle avoidance method for AUV 3D path planning is difficult, and existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, we design an obstacle collision prediction model (CPM). Based on the results of the simulation of obstacles’ inertial motion, the safety of the AUV navigation is evaluated to improve the model’s sensitivity to dynamic obstacles. Then, we enhance the learning ability of the sequence sample data by combining it with a long short-term memory (LSTM) network, thus improving the training efficiency and effect of the algorithm. The trained proximal policy optimization (PPO) network can output reasonable actions in order to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on the CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments. Moreover, it achieves dynamic AUV obstacle avoidance in different three-dimensional unknown environments, providing theoretical and technical support for real path planning.

Since an intensely subjective expert weights and the divergence of opinions among experts exist in the evaluation, this paper proposes a modified multi-expert combined weight (MMCW)-fuzzy comprehensive evaluation (FCE) method. This algorithm preliminarily screened the expert-weight set based on the cluster analysis. After designing the objective function according to the information entropy and relative entropy theory, the multi-expert combined-weight single-objective optimization model was constructed via the weighting coefficient. However, the solution happens to poor convergence in the traditional intelligent algorithm, easily falling into the local optimum. Thus, a differential brain storm optimization (DBSO) algorithm was exploited to figure the established optimal model out for the combined coefficient, and the result was obtained via the hierarchy fuzzy comprehensive evaluation. As taken the AUV as an example, the three-layer evaluation index system was established from the motion control, target recognition, and path planning to assess the AUV intelligence, and the simulations were conducted to validate the proposed algorithm. Conclusively, the MMCW-FCE can quantitively evaluate the intelligence of diverse AUV systems on the algorithm level.

Nowadays, Autonomous Underwater Vehicles (AUV) are used in environmental studies, ocean floor mapping, and measuring water properties. Navigation of these vehicles is one of the most challenging issues due to the unavailability of global positioning system (GPS) signal underwater. Inertial navigation is a method commonly used for underwater navigation. If a low-cost Inertial Measurement Unit (IMU) is used, navigation quality will decline rapidly due to sensor inherent error. Although using a Doppler Velocity Log (DVL) speedometer sensor helps limit this error to some extent, it does not yield acceptable accuracy in low-cost IMUs. Filtering the gyro based on the AUV rotational dynamics model can improve the quality of angular velocity measurements and increase the Inertial Navigation System (INS)-DVL navigation accuracy. The presence of uncertainty in the rotational model parameters reduces the navigation algorithm’s performance. In this paper, at first, a simplified rotational model of AUV is presented, and its parameters are identified utilizing data acquired in real experiments. Then a robust Kalman filter for gyro output is proposed to enhance the navigation algorithm performance in the presence of model parameter uncertainty. To evaluate the performance of the proposed method, non-robust and robust filtered gyro outputs are used in the INS-DVL algorithm, and the results are compared with each other. Two types of the robust Kalman filter, stationary and finite horizon, are invoked. According to the field tests, navigation error decreases by 50% using the stationary robust Kalman filter compared to the non-robust Kalman filter. [ABSTRACT FROM AUTHOR]

This paper addresses the 3D trajectory tracking problem of a novel underactuated underwater vehicle with 4 propellers. A 5-DOF kinematic and dynamic model of the quadrotor underwater vehicle (QUV) is established based on the underwater vehicle and quadrotor unmanned aerial vehicle movement mechanism. A double-loop control structure is then developed. By constructing a Lyapunov function for the outer-loop controller, a 5-DOF trajectory tracking error equation and a velocity virtual control law are obtained. The inner-loop controller is built based on nonsingular integral terminal sliding mode control (NITSMC). Finally, the effectiveness and robustness of our control algorithm are demonstrated through numerical simulations. In particular, given a smooth, second-order derivable 3D reference trajectory, the QUV can quickly track the trajectory and satisfactorily converges in the neighborhood of the expected value in a finite time via NTISMC, which verifies its superiority compared with the other popular backstepping control (BSC) method.

This paper presents a comparative study of docking algorithms intended for non-holonomic autonomous underwater vehicles, docking in funnel-shaped docking stations, operating under the influence of ocean currents. While descriptive surveys have been already reported in the literature, our goal is to compare the most relevant algorithms through realistic Monte Carlo simulations to provide an insight into their performance. To this aim, a new numerical performance indicator is proposed, which, based on the geometry of the manoeuvre, is able to characterize a successful or unsuccessful docking, providing a metric for comparison. The experimental study is carried out using hardware-in-the-loop simulation by means of the Stonefish simulator, including the dynamic/hydrodynamic model of the Sparus II AUV, models of all internal and external sensors, and the collision geometry representing the docking station.

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on ‘two-master-one slave’ cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms. [ABSTRACT FROM AUTHOR]

This article details the design, working principles, and testing of a novel position control mechanism for marine operations or inspection in extreme, hostile, or high-speed turbulent environments where unprecedented speed and agility are necessary. The omnidirectional mechanism consists of a set of counter-rotating blades operating at frequencies high enough to dampen vibrational effects on onboard sensors. Each rotor is individually powered to allow for roll control via relative motor effort and attached to a servo-swashplate mechanism, enabling quick and powerful manipulation of fluid flow direction in the hull's coordinate frame without the need to track rotor position. The mechanism inherently severs blade loads from servo torques, putting all load on the main motors and minimizing servo response time, while exploiting consistent blade momentum to minimize the corresponding force response time. A small-scale force-validating model is fabricated and tested for various force and moment commands. Kinematic and hydrodynamic analyses of the hull and surrounding fluid forces during various blade maneuvers are presented, followed by the mechanical design and kinematic analysis of each subsystem in a small scale model. Experimental results of the small-scale model are presented that verify the concepts presented for the larger-scale model. Finally, an open-loop controller is constructed with decoupled parameters for each degree of freedom. [ABSTRACT FROM AUTHOR]

This paper proposes an offline path planning method based on the Improved Quantum Particle Swarm Optimization (IQPSO) algorithm for Autonomous Underwater Vehicles (AUVs) in the underwater environment. The spherical modelling method is adopted to represent irregular underwater obstacles as spheres with a specified radius. Then, the IQPSO algorithm is developed to solve the problem of the limitations of the convergence and optimization ability of the traditional Quantum Particle Swarm Optimization (QPSO) algorithm and to identify the best path for AUVs. In this algorithm, to satisfy the three factors of path safety, path length and angle change of the path point, the fitness function is constructed to achieve multi-objective optimization. A smooth path is designed using the cubic spline interpolation algorithm. Different scenes or the same scene with different obstacles are designed to verify the effectiveness of the algorithm. The simulation results show that compared with PSO algorithm, QPSO algorithm, EGA algorithm and DENPSO algorithm, the path generated by IQPSO algorithm in various scenes is shorter, smoother and more stable.

This paper presents the detailed modeling and simulation of the dynamic coupling between an autonomous underwater vehicle (AUV) and a manipulator. The modeling processes are described with the incorporation of the most dominating hydrodynamic effects such as added mass, lift and drag forces. The hydrodynamic coefficients are derived using strip theory and are adjusted according to dynamical similarity. A fuzzy decoupling controller (FDC) is proposed for an autonomous underwater vehicle-manipulator system (UVMS) which consists of two subsystems, an underwater vehicle and a manipulator. The proposed controller uses a fuzzy algorithm (FA) to adaptively tune the gain matrix of the error function (EF). The EF is described by the integral sliding surface function. This technique allows the off-diagonal elements developed for decoupling the system to be incorporated in the gain matrix. Tracing the FA and EF back to the principle of feedback linearization, one further obtains evidence about the decoupling and stability of the system. Moreover, a desired trajectory with the consideration of the dynamic coupling of the AUV is designed to reduce the thruster forces and manipulator's torques. This technique provides high performance in terms of tracking error norms and expended energy norms. A major contribution of this study is that it adopts the off-diagonal elements to exploit the dynamic coupling between the degrees of freedom of the subsystem and the dynamic coupling between the two subsystems. Simulation results demonstrate the effectiveness and robustness of the proposed technique in the presence of parameter uncertainties and external disturbances.

Underwater navigation is generally accomplished through the data fusion of INS (Inertial Navigation System) and auxiliary sensors such as DVL (Doppler Velocity Logger) sensor. However, because of the possibility of DVL outage, alternative low-cost solutions are attractive. Among these, one is using vehicle kinetic model information extracted by the Newton-Euler equation to improve INS performance, which is called model-aided navigation. In this paper, only the vehicle translational motion dynamics are used to replace DVL in underwater navigation in DVL outage. The vehicle 3D translational dynamics has been obtained by using general Newton-Euler equations. Integrating these dynamics leads to the calculation of velocities in the body reference frame. Similar to DVL measurements, the calculated velocities (called “Pseudo-DVL” in this paper) are fused with INS. In this paper, a Kalman filter is used for INS/Pseudo-DVL data fusion. Field test data collected by an equipped research AUV called SUT-III is used to evaluate the proposed approach experimentally. Also, a comparison between the integrated navigation of INS-DVL and INS-Pseudo-DVL is performed and the results are provided. The results reveal that despite having low-cost MEMS IMUs, INS/Pseudo-DVL has effectively corrected standalone INS estimation. Finally it is concluded that despite slight degradation of navigation performance, an expensive DVL sensor can be replaced by using the proposed method in DVL outage. [ABSTRACT FROM AUTHOR]

This paper presents the design and experimental assessment of the control system for the UX-1 robot, a novel spherical underwater vehicle for flooded mine tunnel exploration. Propulsion and maneuvering are based on an innovative manifold system. First, the overall design concepts of the robot are presented. Then, a theoretical six degree-of-freedom (DOF) dynamic model of the system is derived. Based on the dynamic model, two control systems have been developed and tested, one based on the principle of nonlinear state feedback linearization and another based on a finite horizon linear quadratic regulator (LQR). A series of experimental tests have been carried out in a controlled environment to experimentally identify the complex parameters of the dynamic model. Furthermore, the two proposed controllers have been tested in underwater path tracking experiments designed to simulate navigation in mine tunnel environments. The experimental results demonstrated the effectiveness of both the proposed controllers and showed that the state feedback linearization controller outperforms the finite horizon LQR controller in terms of robustness and response time, while the LQR appears to be superior in terms of fall time.

An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research.

In this research, a novel autonomous underwater vehicle (AUV) cooperative positioning algorithm is proposed to solve the implementation problem of multi-sensor-fusion applications. Different from the traditional methods [i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), and iteration extended Kalman filter (IEKF)], which have large linearity error under the condition of nonlinear observation equation when multiple AUV are cooperative positioning, the proposed algorithm utilized the Baysis filter to solve the AUV cooperative problem. Factor graph and sum-product (FGS)-based cooperative positioning algorithm is established to mathematically implement the Bayse filter by converting the global function estimation problem into a local function sum-product estimation problem. Furthermore, to improve the performance of the proposed algorithm, a robust data processing method is presented by introducing a transform matrix to the estimated position information. To demonstrate and verify the proposed methods, the simulation and real tests in different scenarios are performed in this research. Compared with the traditional EKF, UKF, and IEKF cooperative positioning algorithm, the positioning error of the proposed improved FGS (IFGS) cooperative positioning algorithm is obviously smaller than that of the other three algorithms. Moreover, the IFGS algorithm can reduce the complexity of the algorithm, available improving the computational speed of the whole system. This proposed algorithm has important theoretical and practical value for the both industry and academic areas.

Aiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control actions. The fractional-order PID controller has been successfully applied to heading control, diving control and path-following system of AUV on sea trial. In addition, the fractional-order closed-loop system has proven to be stable. By comparing simulations and experiments, the satisfactory performance, such as overshoot, settling time and steady-state error, has been achieved. The cloud-model-based quantum genetic algorithm (CQGA) is employed to tune coefficients of fractional-order PID controller. The quantum bits and quantum superposition states avoid the pressure of selection and maintain the diversity of population in chromosome coding. Due to the randomness and stability tendency of cloud droplets, the cloud crossover operator and the cloud mutation operator can effectively overcome the shortcomings of premature and slow searching speed. Numerical simulations show that the CQGA is more efficient to find the optimal coefficients of fractional-order PID controller than GA.

This paper studies the problem of spatial path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties and input saturation taken into account. Initially, the reduced-order extended state observes (ESOs) are introduced to estimate and compensate all lumped uncertainties due to the model parameters perturbations, unmodeled dynamics, environmental disturbances, and nonlinear hydrodynamic damping terms. Furthermore, the spatial path-following control strategy is established by combining with backstepping, integral sliding mode control, and estimator to cope with the position, attitude, and linear velocity tracking of the AUV. Specifically, the dynamic surface control (DSC) technique is utilized to achieve satisfactory differential performance and avoid the “explosion of complexity.” Additionally, the auxiliary dynamic compensator is presented to analyze the effect of input saturation, and the states of the auxiliary design system are used to develop the controller. It is easily proved that the proposed control scheme can guarantee that all signals of the closed-loop system are globally stable through the Lyapunov theorem, with the tracking errors converging to an arbitrarily small neighborhood of zero. Finally, the numerical simulation results are carried out to illustrate the effectiveness of the proposed controller.

Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision. In this research, a novel autonomous underwater vehicle (AUV) CP algorithm is proposed to enhance the global localization accuracy of the follower AUV. However, in traditional CP algorithm, the positioning error is large under the condition that the outlier data exists in the observation, which happens commonly. So in this research, a novel CP algorithm based on the factor graph and maximum correntropy(FGMC) for AUV is proposed to enhance the global localization accuracy of the AUV. Different from the traditional algorithms, this presented FGMC-based CP algorithm implements mathematically the Bayes filter by converting the global function estimation problem into the local one. And furthermore, the maximum correntropy is used as the cost function in the factor graph to estimation problem, this can reduce the influence of outliers on positioning accuracy. FGMC based cooperative positioning algorithm is established to mathematically implement the Bayes filter by converting the global function estimation problem into local function estimation problem. Furthermore, the maximum correntropy is used as the cost function in the factor graph to estimate the variables. To demonstrate and verify the proposed algorithm, simulation and real tests in different scenarios are performed in this research. Compared with the traditional CP algorithms, the positioning error of the proposed FGMC cooperative positioning algorithm is obviously smaller than that of the other algorithms.

INS-DVL integration is a common method for underwater navigation. However, inherent errors of sensors, especially in MEMS IMUs, lead to inaccuracies in estimating the position and attitude. In this paper, dynamic motion model of AUV is used to improve MEMS INS-DVL navigation. In this method, which is called model-aided or model-based navigation, the information of the kinetic model of the vehicle (obtained from Newton–Euler equations) is used to improve the navigation performance. Previous model-aided navigation studies about AUVs have been focused on the translational dynamic model of vehicles. As the best of our knowledge, this paper is the first one which suggests using a rotational motion model of AUVs to improve the navigation performance and at the same time, reports successful implementation of method through field-testing. The utilized dynamic model, describes the rotational motion of AUV in a 3DOF form. Information extracted from the rotational dynamic model and the outputs of gyroscopes, will be fused in a secondary Kalman filter. So, before the fusion with auxiliary sensors in the main Kalman filter, the gyroscopes outputs have been improved by the rotational motion model. Therefore, the overall output will be better than the outputs of gyroscopes by itself. The main Kalman filter is a perturbation-based Kalman filter which is employed for data fusion. This algorithm is implemented on real data collected from field-test. The results show that this inexpensive method which requires no additional equipment was truly effective in improving navigation performance. Using this model, the accuracy of navigation was improved 5 to 10 times. [ABSTRACT FROM AUTHOR]

A Doppler velocity log (DVL) provides a three-dimensional representation of the velocity of an underwater vehicle to the vehicle’s DVL-aided inertial navigation system (INS). It is a key element in determining the precision of the INS. This paper presents an algorithm for reducing the error in the DVL as a way of improving the navigation of an autonomous underwater vehicle (AUV). We also investigate and analyze the effect that the seafloor’s deviations have on DVL measurement error. Finally, the performance of the proposed algorithm is examined through both AUV simulation and experimentally. The results show that the proposed technique can indeed effectively correct DVL errors. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) play an important role in subaqueous construction and oceanographic survey, in which AUV self-localization is a crucial component. Current localization methods mainly depend on expensive acoustic positioning systems, which limit the wide application of AUVs. In this paper, referring to the low-cost visual localization method and focusing on challenges caused by underwater environment, we propose an underwater self-localization method based on Pseudo-3D vision-inertia for AUVs. The proposed method merges depth information into 2D visual image to achieve continuous and robust localization under dramatically changing underwater environment. In order to decrease errors, we propose an online fusion method based on tightly-coupled nonlinear optimization to fuse the measurements of the pre-integrated inertial measurement unit and the observations from the down-looking camera. We also optimize four degrees-of-freedom pose graph to enhance the global consistency and design an online loop detection module to realize the underwater relocalization. In addition, we develop a low-cost, portable, and small volume sensor suite for underwater vehicle localization and test the proposed self-localization method. We test the proposed method in the underwater environment using the custom-made sensor suite, and the experimental results demonstrate the effectiveness of the proposed method under dramatically changing underwater environment. [ABSTRACT FROM AUTHOR]

Using autonomous underwater vehicles (AUVs) for mapping underwater topography of sea-ice and icebergs, or detecting keels of ice ridges, is foreseen as enabling technology in future arctic marine operations. Wind, current, and Coriolis forces affect an iceberg's trajectory, making automated mapping difficult. This paper presents a method aiming at enabling autonomous iceberg mapping using AUVs equipped with a multibeam echosounder by estimating the position and orientation of the iceberg. The method is based on a bathymetric simultaneous localization and mapping (SLAM) algorithm, namely, the bathymetric distributed particle filter SLAM (BPSLAM) algorithm. The proposed method estimates the AUV's pose in an iceberg-fixed coordinate system. The relative states can be used for both guiding the vehicle to achieve complete coverage, as well as estimation of a consistent iceberg topography. The algorithm also provides an estimate of the iceberg's drift velocity - an important parameter for the AUV trajectory planning as well as any related ice management (IM) operations. Two new weighting algorithms for the BPSLAM method are proposed, enabling batch processing of multibeam echosounder (MBE) measurements to ensure real-time operation without discarding information. The proposed method is demonstrated using a real iceberg topography taken from the PERD iceberg sightings database, with simulated AUV and MBE range measurements. The algorithm is also evaluated on a real world bathymetric dataset, collected using the HUGIN HUS AUV.

In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols. [ABSTRACT FROM AUTHOR]

Path planning is an important problem in autonomous control technology. This paper aims to overcome the shortcomings of the wolf pack algorithm (WPA), such as slow rate of convergence and low convergence precision, by improving the three intelligent behaviors of the WPA, namely, scouting, summoning, and beleaguering. To improve the scouting behavior, interactive scouting is proposed to increase the interactivity among wolf pack. Furthermore, to improve the summoning behavior, a prey-based adaptive step model is established to improve the searching ability. Finally, calculation rules of new beleaguering behavior are designed, which enhance the local fine search ability considerably. A fast path planning method based on dubins path was proposed, which applied the dubins path planning to meet angle control constraint and tunes the turning radius to meet control constraint. The dubins path planning method based on the modified WPA is proposed by establishing the underwater environment threat model under the condition of autonomous underwater vehicle constraint. The path between the path points is the shortest, the threat is minimal, and the energy consumption is the least without the consideration of ocean current. Simulation results show that the modified WPA has a high rate of convergence and good local search capability in the high-precision, high-dimensional, and multi-peak function; moreover, it does not converge prematurely.

Owning to its extremely long endurance, small size, cost-effective employment, easy, and fast transported properties, the merits of autonomous underwater vehicles (AUVs) exceed those of manned underwater vehicles. Therefore, it has become an indispensable member in navies throughout the world, as well as with oceanographic research, commercial resource explorations, and especially the underwater search and rescue (SAR) sectors. Up to date, the tragic MH370 and QZ8501 flight incidents have dramatically increasing the awareness of high-performance SAR equipment, which in turn will stimulate the states party to the incident to urgently upgrade their shallow- and deep-sea SAR equipment. To extend the range (endurance) and shorter the searching period for the next generation SAR AUV, a sea water cooled high-power density magnetically slotless permanent magnet brushless motor is proposed and integrated with pump-jet’s impeller to achieve designated shaftless pump-jet propulsor. In this paper, the developed shaftless pump-jet propulsion system is applied in Naval University of Engineering series multi-purpose long-range and high-speed deep-sea AUV, which could provide excellent performance as a SAR platform. [ABSTRACT FROM PUBLISHER]