Autonomous Underwater Vehicle Docking Research Articles

Underwater fluid-driven soft dock for dynamic recovery of AUVs with improved pose tolerance

Recovering autonomous underwater vehicles (AUVs) by a mobile platform is a complex challenge. Traditional docking stations require strict pose deviation from the underwater vehicle. To overcome this issue, we propose a novel solution: an in-situ fluid-driven soft dock. This system comprises three soft robotic manipulators that possess guiding, capturing, and securing functionalities, respectively. We present the design, fabrication, and control methods for each manipulator considering the requirements of underwater docking operations. The theoretically allowable docking pose deviation range during AUV docking is analyzed. Additionally, the required force of soft grippers was evaluated through simulation and equivalent experimental tests. To enable dynamic recovery of AUVs while the soft robotic manipulators remain mobile on the experimental platform, we introduce a hydraulic control method based on attitude and pressure feedback. The test results indicate that the designed soft manipulators can operate and maintain good sealing performance under an external pressure of no less than 11 MPa. Moreover, the soft dock successfully recovered and secured AUVs of two different diameters in 20 dynamic tests, achieving a 90% success rate with a maximum AUV velocity of 1.6 m/s.

Collision dynamics in AUV docking with conical hood type dock: influencing factors and performance analysis

ABSTRACT To enhance docking safety, the collision dynamics during autonomous underwater vehicle (AUV) docking is investigated. Various factors such as dock structures, motion conditions, and related parameters are considered. The relationship between docking collision force and key parameters is unveiled by the mathematical model based on the restoration coefficient method and equivalent spring damping method. Multivariate nonlinear fitting functions are proposed to evaluate collision force and docking time. Furthermore, the deductions made in the research are supported by a preliminary test and the groundwork for further analysis is laid. The study demonstrates the impacts of various factors on docking, including different docking cone hood profiles, structures, velocities, accelerations, eccentricities, traction forces, angles, and force indices. By clarifying relevant mechanism, docking parameters can be achieved to meet diverse docking requirements and enhance the device's lifespan. The research offers valuable insights for achieving smoother AUV docking with conical hood type docks.

Deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle

In this paper, a deep learning-based underwater positioning scheme is proposed to achieve robust feature tracking of an autonomous underwater vehicle (AUV) in sonar image during dynamic docking. To address the issues that the distorted feature and acoustic noises lead significant difficulty to detection and tracking of AUV in acoustic image during dynamic docking, first, a pre-trained You Only Look Once (YOLO) network is applied to detect both body and head features of AUV. Second, we introduce an Intersection Over Union (IOU) match-based backend which preliminarily filters the error detections of AUV head based on the rigid relationship between body and head of AUV. Subsequently, Simple Online and Realtime Tracking with a deep association metric (DeepSort) is utilized to achieve track matching of all detection results including error detections and real target. Moreover, a scoring mechanism is presented to further remove the unfiltered error detections based on the motion tendency of detection tracks. Experiment result shows that the proposed scheme enables real-time and robust feature tracking of AUV with the interference of feature distortion, reverberation and environmental noises.

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

PurposeDocking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.Design/methodology/approachThis paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.FindingsThe authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.Originality/valueThis fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Two-Step Adaptive Control for Planar Type Docking of Autonomous Underwater Vehicle

Planar type docking enables a convenient underwater energy supply for irregularly shaped autonomous underwater vehicles (AUVs), but the corresponding control method is still challenging. Conventional control methods for torpedo-shaped AUVs are not suitable for planar type docking due to the significant differences in system structures and motion characteristics. This paper proposes a two-step adaptive control method to solve the planar type docking problem. The method makes a seamless combination of horizontal dynamic positioning and visual servo docking considering ocean current disturbance. The current disturbance is estimated and canceled in the pre-docking step using a current observer, and the positioning error is further compensated for by the vertical visual servo technique in the docking step. Reduced order dynamic models are distinctively established for different docking steps according to the motion characteristics, based on which the dynamic controllers are designed considering the model parameter uncertainties. Simulation is conducted with an initial distance of 10 m in the horizontal direction and 3 m in depth. Stable and accurate dynamic positioning under up to 0.4 m/s of current disturbances with different directions is validated. A 0.5 m lateral positioning error is successfully compensated for by the visual servo docking step. The proposed control method provides a valuable reference for similar types of docking application.

Acoustic communication and imaging sonar guided AUV docking: system infrastructure, docking methodology and lake trials

Autonomous underwater vehicle (AUV) docking technique attracts attention which leverages the long-term, on-station vehicle launch and recovery for the underwater missions. In this paper, an acoustic communication and imaging sonar guided docking method is proposed to improve the docking accuracy for AUV. In docking system, multibeam forward looking sonar (MFLS) is deployed on the submerged docking station to reduce the noise in acoustic images rather than being fixed on AUV, and the ultra short base line (USBL) transceiver with acoustic communication modules is used to achieve positioning while AUV is out of the view of MFLS. For the docking methodology, a dedicated three-stage docking strategy is presented to guide AUV to the docking station. In the homing stage, AUV navigates to a preset homing place not very far from the docking station via the way-point guidance and self-positioning of the on-board inertial navigation system (INS). In the next stage, USBL provides the relative position for AUV controller via acoustic channel. Moreover, an exponential decay model based filtering algorithm is proposed to eliminate the positioning outliers of USBL. It is expected that AUV will be guided into the perceptual field of MFLS in this stage. In the final stage, the docking accuracy in short range should be improved while USBL may generate outliers due to the louder noise. In this case, the relative positioning is implemented by using kernelized correlation filter (KCF), which is a fast objective tracking algorithm to keep track of the AUV feature in acoustic image until AUV navigates into the docking station or out of the view of sensor. To validate feasibility and performance of the proposed system infrastructure and docking methodology, the docking results are analyzed by contrast with the experiments only use USBL during lake trials.

Optimizing wireless acoustic energy harvesting and communication with passive markers underwater

To enhance functionality of Acoustic Identification Tags (AID), passive markers designed as landmarks for Autonomous Underwater Vehicles (AUVs) [Satish et al., JASA 149 (2021)], the development of an accompanying battery free active communication platform was explored [Bhardwaj et al., JASA 152, 4 (2022)]. This platform utilizes custom piezoelectric transducers impedance matched for broadband operation, enabling concurrent acoustic energy harvesting and fast backscatter communication [Allam et al., SPIE 31, 9 (2022)]. The combination of these active and passive devices can improve AUV localization and information transfer for specific applications such as short-range search missions or AUV homing and docking. The design of this platform for underwater operations requires a balance of parameters such as center frequency and piezoelectric element dimensions to achieve a high range of operation, power efficiency and data rates, while minimizing attenuation and spreading losses challenges. The design process for piezo selection, acoustic and electrical impedance matching, transducer casing design, and the experimental evaluation of the chosen 350 kHz design will be discussed. This approach was validated experimentally to quantify performance metrics such as the maximal range, data rates using various standard communication such as Frequency Shift Keying (FSK) and Amplitude Modulation (AM).

Passive underwater Acoustic IDentification (AID) tags for enhancing Autonomous Underwater Vehicle (AUV) navigation during docking or homing operations

Autonomous underwater vehicle (AUV) navigation requires accurate positioning information from the surrounding environment, especially for tasks such as AUV homing or docking operations. Previous literature has introduced a class of low-cost passive underwater acoustic markers, termed Acoustic IDentification (AID) tags [Satish and Sabra, J. Acoust. Soc. Am. 149(5), 3387–3405 (2021)] built of multi-layer shells with different acoustic properties and thicknesses to generate a uniquely engineered acoustic signature, composed of the multiple reflections created by the layer interfaces. These passive AID tags can be detected by an AUV instrumented with a high-frequency sonar transducer at significantly greater distances than conventional optical methods, especially in turbid waters. Additionally using AID tags as navigation-aid can also alleviate the need of relying on active acoustic transponders. An implementation of a constellation of AID tags enabling fine underwater positioning an AUV towards a docking station or for homing purposes will be presented to provide proof of concept. Furthermore, the optimization of the design of the AID tags for this application aswell as specific signal processing detection methodologies to improve thedetectability of AID tags in the presence of interfering signals (e.g., clutter) will be discussed. [Work supported by the Office of Naval Research].

Study on influencing factors of hydrodynamics based on AUV docking with conical dock

It is necessary to investigate the dynamic performance during autonomous underwater vehicle (AUV) underwater docking to aid in control and to enhance docking safety. Therefore, in this study, the docking hydrodynamic characteristics (including the docking system’s streamline, velocity vector, and surface pressure during the overall docking process) of the AUV with conical hood dock are determined by using the dynamic grid technology, and the water drag force situations of the AUV docking with conical hood dock are studied from the perspectives of different velocities, accelerations, navigation modes, and structures. Additionally, the multiple nonlinear regression fits and the preliminary docking test were examined. Furthermore, brief inferences obtained are as follows: First, the maximum pressure is situated at the upstream surface of the dock conical hood and the head of the AUV, and the maximum rotation angle of the streamline is situated at the outermost ring of dock conical hood. Within a specified range, the rotation angle of the streamline affected by the conical hood progressively declines as the AUV docks deeper into the conical hood. Second, low velocity uniform docking, deceleration docking, and chase docking can reduce the drag force to a certain extent during docking. Finally, both arc shape and mesh structure can decrease the water drag force of docking to a specified degree. This research provides a theoretical basis and reference methods for the dynamic research of the docking system, and other related research can be carried out through the methods and results of this research.

Integrated approach to AUV docking based on nonlinear offset-free model predictive control

This study proposes an integrated docking approach for autonomous underwater vehicle (AUV), which directly generate control force and torque from visual guidance features to drive precise AUV docking. Accurate docking control has been constructed as nonlinear offset-free model predictive control (OFNMPC) problem to effectively deal with external disturbances, model mismatch, and various other constraints. For the problem of state and disturbance estimation required by OFNMPC, observers have been presented based on the extended Kalman filter (EKF) and moving horizon estimation (MHE). A visual observation model is proposed so that the observer can directly use visual measurements. OFNMPC was integrated with EKF and MHE, and the proposed OFNMPC-EKF and OFNMPC-MHE docking approaches were applied to an actual AUV model. The simulation results show that the proposed approaches could drive the AUV to complete accurate docking along a reasonable trajectory based on visual measurements. The navigation, guidance, and control functions of docking were effectively integrated. It was observed that in the presence of external disturbances and measurement noise, OFNMPC-EKF has better robustness.

Analyzing the impact of disturbances on the AUV docking control system

We consider the problem of docking the autonomous underwater vehicle (AUV) to the seabed container. A two-level control system was previously developed using the mathematical model of the AUV dynamics. We further improve the mathematical model with the model of the AUV Computer Vision system, which is based on cameras and specialized markers installed on the container. In this paper, the studies are supplemented by considering the influence of two disturbance factors. The first one is the pixel coordinate error of the detected markers? Which is internal to the system. The second one is the external stationary ocean current. We study system performance in the vicinity of these factors and determine the acceptable docking conditions.

Autonomous underwater vehicle docking system for energy and data transmission in cabled ocean observatory networks

Cabled ocean observatory networks (COON) are used for long-term all-weather observation of submarine scientific data, which contribute to low-carbon ocean energy research. Autonomous underwater vehicles (AUV) with clean energy can provide active search capabilities by connecting with the docking station (DS) on the COON to complete energy and data transmission in long-term detection tasks. The AUV is guided by optical active landmarks and a vision system for short-range docking. In this study, we propose an active landmarks tracking framework to solve the problem of detecting failure caused by incomplete observation of landmarks. First, a two-stage docking algorithm based on CNN is used to estimate the 3D relative position and orientation between DS and AUV during docking, including detect phase and PnP pose estimator. Then extended Kalman filter and Hungarian matching algorithm are introduced to improve the robustness of the algorithm. The reliability of the vision-based short-range docking algorithm is verified in the pool, and the robustness of the algorithm to the field environment is shown in the lake field experiment combined with long-range guidance. The experimental results indicate that the algorithm framework can effectively leverage the landmarks information and enhance the scope of the visual guidance algorithm.

Estimation of Positions and Poses of Autonomous Underwater Vehicle Relative to Docking Station Based on Adaptive Extraction of Visual Guidance Features

The underwater docking of autonomous underwater vehicles (AUVs) is conducive to energy supply and data exchange. A vision-based high-precision estimation of “the positions and poses of an AUV relative to a docking station” (PPARD) is a necessary condition for successful docking. Classical binarization methods have a low success rate in extracting guidance features from fuzzy underwater images, resulting in an insufficient stability of the PPARD estimation. Based on the fact that guidance lamps are blue strong point light sources, this study proposes an adaptive calculation method of binary threshold for the guidance image. To decrease the failure of guidance feature extraction, a guidance image enhancement method is proposed to strengthen the characteristic that the guidance lamps are strong point light sources with a certain area. The PPARD is estimated through solving the minimum value of the imaging error function for the vision-based extracted guidance features. The experimental results showed that the absolute estimation error for each degree of freedom in the PPARD was at most 10%, which was lower than that of the orthogonal iteration (OI) method. In addition, the proposed guidance feature extraction method proved to be better than the classical methods, with the extraction success rate reaching 87.99%.

Smart vector-inspired optical vision guiding method for autonomous underwater vehicle docking and formation.

A smart vector-inspired optical vision guiding (VIOVG) method for autonomous underwater vehicle (AUV) docking and formation is proposed. Unlike traditional optical guiding methods based on LED arrays, the method is inspired by a vector, and uses four laser diodes to form a wing-light pattern to realize smart optical guiding for AUVs. Due to the light scattering effect from water, the four laser diodes can emit four bright and slim laser beams in water, which can be captured by underwater cameras. The intersections of the laser beams form wing-lights as markers for AUV distance and pose estimation. The wing-light pattern is easily tuned and extended by changing the propagation direction and the power of the laser beams beyond the limitation of the physical size of the AUV or docking station. The simulations and experiments show that the proposed method can achieve high precision positioning. A relative distance error of 3.35% is achieved in a positioning experiment at ∼10 m. It has great potential for AUV docking and formation, especially for small AUVs.

Mission Management Technique for Multi-sensor-based AUV Docking

This study presents a mission management technique that is a key component of underwater docking system used to expand the operating range of autonomous underwater vehicle (AUV). We analyzed the docking scenario and AUV operating environment, defining the feasible initial area (FIA) level, event level, and global path (GP) command to improve the rate of docking success and AUV safety. Non-holonomic constraints, mounted sensor characteristic, AUV and mission state, and AUV behavior were considered. Using AUV and docking station, we conducted experiments on land and at sea. The first test was conducted on land to prevent loss and damage of the AUV and verify stability and interconnection with other algorithms; it performed well in normal and abnormal situations. Subsequently, we attempted to dock under the sea and verified its performance; it also worked well in a sea environment. In this study, we presented the mission management technique and showed its performance. We demonstrated AUV docking with this algorithm and verified that the rate of docking success was higher compared to those obtained in other studies.

Docking to an underwater suspended charging station: Systematic design and experimental tests

Using seafloor docking stations to automatedly recover and recharge autonomous underwater vehicles (AUV) is an emerging technology to solve the problem of energy limitation for persistent observation. However, this operational modality would constrain the range of AUV activity within a certain radius centered of the seafloor docking station. To increase the recovery flexibility of AUV, this paper demonstrates a concept of docking to a suspended underwater charging station, which is fixed on a taut rope from a surface vessel. This study provides some critical design steps of the AUV docking system, including the construction of the platforms, the guidance method, the collision analysis of docking operation, as well as the rotation-adaptive contactless power/data transfer method. The feasibility of the system was verified in Qingjiang River. Aided by a re-attempt mechanism, the success rate of docking to the cone-type entrance of the suspended station is 100% (39 of 39 tests) after the surface vessel was anchored and the operating depth was over 30-m. The maximum contactless charging power is 882 W with an efficiency of 81.3%, and the maximum data transmission rate is of approximately 41.1 Mbps at different roll angle of AUV.

Fusion guiding technology solution and algorithm for underwater docking of autonomous underwater vehicles

Fusion guiding technology solution and algorithm for underwater docking of autonomous underwater vehicles

Event-Based Circular Detection for AUV Docking Based on Spiking Neural Network.

In this paper, a circular objects detection method for Autonomous Underwater Vehicle (AUV) docking is proposed, based on the Dynamic Vision Sensor (DVS) and the Spiking Neural Network (SNN) framework. In contrast to the related work, the proposed method not only avoids motion blur caused by frame-based recognition during docking procedure but also reduces data redundancy with limited on-chip resources. First, four coplanar and rectangular constrained circular light sources are constructed as the docking landmark. By combining asynchronous Hough circle transform with the SNN model, the coordinates of landmarks in the image are detected. Second, a Perspective-4-Point (P4P) algorithm is utilized to calculate the relative pose between AUV and the landmark. In addition, a spatiotemporal filter is also used to eliminate noises generated by the background. Finally, experimental results are demonstrated from both software simulation and experimental pool, respectively, to verify the proposed method. It is concluded that the proposed method achieves better performance in accuracy and efficiency in underwater docking scenarios.

Reimagining Autonomous Underwater Vehicle Charging Stations with Wave Energy

The vast capabilities of autonomous underwater vehicles (AUVs)—such as in assisting scientific research, conducting military tasks, and repairing oil pipelines—are limited by high operating costs and the relative inaccessibility of power in the open ocean. Wave powered AUV charging stations may address these issues. With projected increases in usage of AUVs globally in the next five years, AUV charging stations can enable less expensive and longer AUV missions. This paper summarizes the design process and investigates the feasibility of a wave powered, mobile AUV charging station, including the choice of a wave energy converter and AUV docking station as well as the ability to integrate the charging station with an autonomous surface vehicle. The charging station proposed in this paper meets many different commercial, scientific, and defense needs, including continuous power availability, data transmission capabilities, and mobility. It will be positioned as a hub for AUV operations, enabling missions to run autonomously with no support ship. The potential market for this design is very promising, with an estimated $1.64 million market size just for AUV technologies by 2025.

An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking.

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.