Journal articles: 'Hybrid AUV'

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Caffaz, A., A. Caiti, G. Casalino, and A. Turetta. "The Hybrid Glider/AUV Folaga." IEEE Robotics & Automation Magazine 17, no. 1 (March 2010): 31–44. http://dx.doi.org/10.1109/mra.2010.935791.

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Li, Hui, and Brian Williams. "Hybrid Planning with Temporally Extended Goals for Sustainable Ocean Observing." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1365–70. http://dx.doi.org/10.1609/aaai.v25i1.7800.

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A challenge to modeling and monitoring the health of the ocean environment is that it is largely under sensed and difficult to sense remotely. Autonomous underwater vehicles (AUVs) can improve observability, for example of algal bloom regions, ocean acidification, and ocean circulation. This AUV paradigm, however, requires robust operation that is cost effective and responsive to the environment. To achieve low cost we generate operational sequences automatically from science goals, and achieve robustness by reasoning about the discrete and continuous effects of actions. We introduce Kongming2, a generative planner for hybrid systems with temporally extended goals (TEGs) and temporally flexible actions. It takes as input high level goals and outputs trajectories and actions of the hybrid system, for example an AUV. Kongming2 makes two major extensions to Kongming1: planning for TEGs, and planning with temporally flexible actions. We demonstrated a proof of concept of the planner in the Atlantic ocean on Odyssey IV, an AUV designed and built by the MIT AUV Lab at Sea Grant.

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Chen, Ziyun, Dengsheng Zhang, Chengxiang Wang, and Qixin Sha. "Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search." Electronics 12, no. 22 (November 8, 2023): 4575. http://dx.doi.org/10.3390/electronics12224575.

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For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

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Liu, Tong Hui, Yan Hui Wang, and Zhu Guang. "Analysis of Hydrodynamic Noise around Acoustic Modem on the AUV Using Hybrid LES-Lighthill Method." Advanced Materials Research 546-547 (July 2012): 176–81. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.176.

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Acoustic modem is one of the most important sensors on the deep sea AUV. A good performance of the acoustic modem has great effect on the safety and operation of deep sea AUV. According to the construction of deep sea AUV, acoustic modem is installed in the cavity behind the GPS antenna, it would cause flow noise around the acoustic modem and thus interference will occur when the communication is established between deep sea AUV and surface. Flow-induced noise around the acoustic modem is studied in this paper. Firstly the unsteady flow around the acoustic modem is studied by large eddy simulation (LES) method. Secondly based on the resolved information of flow field, acoustic noise distribution is studied using equivalent acoustic sources method. Compared with different positions of GPS antenna, flow noise distribution around the acoustic modem and the effect on communication is investigated. Then acoustic modem layout on the AUV is optimized.

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Hien, Ngo Van, Ngo Van He, Van-Thuan Truong, and Ngoc-Tam Bui. "A MBSE Application to Controllers of Autonomous Underwater Vehicles Based on Model-Driven Architecture Concepts." Applied Sciences 10, no. 22 (November 23, 2020): 8293. http://dx.doi.org/10.3390/app10228293.

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In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language (SysML), the extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, and it can be reused for designing controllers of various AUV types. The dynamic model and control structure of AUVs were combined with the specialization of MDA concepts as follows. The computation-independent model (CIM) was specified by the use-case model combined with the EKF/UKF algorithms and hybrid automata to intensively gather the control requirements. Then, the platform-independent model (PIM) was specialized using the real-time UML/SysML to design the capsule collaboration of control and its connections. The detailed PIM was subsequently converted into the platform-specific model (PSM) using open-source platforms to promptly realize the AUV controller. On the basis of the proposed hybrid model, a planar trajectory-tracking controller, which allows a miniature torpedo-shaped AUV to autonomously track the desired planar trajectory, was implemented and evaluated, and shown to have good feasibility.

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Sahoo, Sarada Prasanna, Bikramaditya Das, Bibhuti Bhusan Pati, Fausto Pedro Garcia Marquez, and Isaac Segovia Ramirez. "Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles." Journal of Marine Science and Engineering 11, no. 4 (March 31, 2023): 761. http://dx.doi.org/10.3390/jmse11040761.

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This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.

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Bu, Fanfeng, Hanjiang Luo, Saisai Ma, Xiang Li, Rukhsana Ruby, and Guangjie Han. "AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning." Sensors 23, no. 2 (January 4, 2023): 578. http://dx.doi.org/10.3390/s23020578.

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Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

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Bond, Todd, Jane Prince, Dianne L. McLean, and Julian C. Partridge. "Comparing the Utility of Industry ROV and Hybrid-AUV Imagery for Surveys of Fish Along a Subsea Pipeline." Marine Technology Society Journal 54, no. 3 (May 1, 2020): 33–42. http://dx.doi.org/10.4031/mtsj.54.3.5.

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AbstractIn recent years, video footage obtained from routine industry surveys using remotely operated vehicles (ROVs) has been used to assess fish assemblages associated with offshore oil and gas infrastructure. However, as industry moves towards using autonomous underwater vehicles (AUVs), it is important to understand how such a change may affect assessments of fish assemblages and whether these data collected from AUVs can be directly compared to those collected from historic and future ROV footage. In an extremely rare opportunity, we compare fish assemblages surveyed by an industrial ROV and an industrial hybrid-AUV (H-AUV) along the same 2,060-m section of subsea pipeline, at the same time of day, within 1 day of each other. A total of 206 transects, each 20 m in length, were analyzed, recording 406 fish from 10 species. The H-AUV recorded all 10 fish species, while the ROV recorded seven. Mean species richness was ~4% higher for H-AUV, and mean abundance was ~21% higher for ROV; these differences can be considered negligible. Multivariate analysis revealed nonsignificant differences in species composition between survey methods. This result suggests that future surveys of fish at this pipeline using a similar H-AUV can yield comparable results to ROV and, as such, facilitate comparison to historical ROV imagery. Future surveys of pipelines with AUV should consider adding extra lighting and high-definition cameras onto booms similar to those operating on ROVs, which will provide a useful field of view into pipeline spans and make it easier to identify fish. Our study provides insight into the utility and comparability of industry ROV and AUV pipeline inspection footage for the assessment of fish assemblages associated with subsea pipelines, which is used to inform policies and practices on the installation and decommissioning of subsea infrastructure.

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Nie, Yunli, Xin Luan, Yan Huang, Libin Du, Dalei Song, and Xiuyan Liu. "Microstructure Turbulence Measurement in the Northern South China Sea from a Long-Range Hybrid AUV." Sensors 23, no. 4 (February 10, 2023): 2014. http://dx.doi.org/10.3390/s23042014.

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This study describes the development of a long-range hybrid autonomous underwater vehicle (AUV) for ocean turbulence measurement. It is a unique instrument, combining the characteristics of the conventional AUV and the buoyancy-driven glider, with a variety of flexible motion modes, such as cruise mode, glider mode, drift mode, and combination of multiple motion modes. The hybrid AUV was used for continuous turbulence measurement in the continental slope of the northern South China Sea in 2020. A total of ten continuous profiles were completed covering a horizontal span of 25 Km and a depth of 200 m. The hybrid AUV was operated in the combined glider and cruise mode. The hybrid AUV’s flight performance was stable and satisfied the requirement for turbulence observation. The measured velocity shears from both probes were in good agreement, and the noise-reduced shear spectra were in excellent agreement with the Nasmyth spectrum. The water column in the study area was highly stratified, with a thick thermocline. The dissipation rate (ε) varied from 1.41 × 10−10 to 4.18 × 10−7 W·kg−1. In the surface mixed layer, high values of ε (10−9∼10−8 W·kg−1) were observed toward the water surface. In the thermocline, ε was 10−9.5∼10−9 W·kg−1, which was smaller than the level of the surface mixed layer. This result was mainly because of the strong “barrier”-like thermocline, which damped the transmission of wind and heat energy from the surface mixed layer to the deep layer. Overall, this study demonstrates the utility of hybrid AUVs for collecting oceanic turbulence measurements. They are a powerful addition to traditional turbulence instruments, as they make it possible to survey large areas to obtain high-quality and high-resolution data in both vertical and horizontal directions over long durations.

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Zuo, Mingjiu, Guandao Wang, Yongxin Xiao, and Gong Xiang. "A Unified Approach for Underwater Homing and Docking of over-Actuated AUV." Journal of Marine Science and Engineering 9, no. 8 (August 17, 2021): 884. http://dx.doi.org/10.3390/jmse9080884.

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During the implementation of time-consuming tasks such as underwater observation or detection, AUV has to face a difficult and urgent problem that its working duration is greatly shortened by the limited energy stored in the battery device. To solve the power problem, a docking station is installed underwater for AUV charging its battery. However, to realize the automatic underwater charging of AUV via a docking station, the accurate and efficient completion of underwater homing and docking is required for AUV. Underwater automatic homing and docking system is of great significance to improve work efficiency and prolong the endurance of AUV save cost. In this paper, a unified approach that involves such as task planning, guidance and control design, thrust allocation has been proposed to provide a complete solution to the problem of homing and docking of an over-actuated AUV. The task-based hybrid target point/line planning and following strategy are proposed for AUV homing and docking. At the beginning of homing, AUV is planned to follow a straight line via the line of sight (LoS) method. Afterward, AUV starts to follow multiple predefined target points until reaching the docking station. At the final stage of docking (within 10 m), a dedicated computer vision algorithm is applied to detect a newly designed LED light array fixed on the docking station to provide accurate guidance for the AUV to dock. The sliding mode control technique is used for the motion control of the AUV allowing robustness. As the AUV configured with eight thrusters is over-actuated, the problem of the thrust allocation is very important and successfully solved using the quadratic programming (QP) optimization method. Finally, the simulations of homing and docking tasks using the AUV are accomplished to verify the proposed approach.

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Fyrvik, Torbjørn R., Jens E. Bremnes, and Asgeir J. Sørensen. "Hybrid Tracking Controller for an ASV Providing Mission Support for an AUV." IFAC-PapersOnLine 55, no. 31 (2022): 91–97. http://dx.doi.org/10.1016/j.ifacol.2022.10.414.

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Wu, Baoju, Xiaowei Han, and Nanmu Hui. "System Identification and Controller Design of a Novel Autonomous Underwater Vehicle." Machines 9, no. 6 (May 26, 2021): 109. http://dx.doi.org/10.3390/machines9060109.

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Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method.

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Hien, Ngo Van, Van-Thuan Truong, and Ngoc-Tam Bui. "A Model-Driven Realization of AUV Controllers Based on the MDA/MBSE Approach." Journal of Advanced Transportation 2020 (October 25, 2020): 1–14. http://dx.doi.org/10.1155/2020/8848776.

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This paper introduces a model-driven control realization, which is based on the systems engineering concepts of the model-driven architecture (MDA)/model-based systems engineering (MBSE) approach combined with the real-time UML/SysML, extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, in order to conveniently deploy controllers of autonomous underwater vehicles (AUVs). This model also creates a real-time communication pattern, which can permit the designed components to be customizable and reusable in new application developments of different AUV types. The paper brings out stepwise adapted AUV dynamics for control that are then combined with the specialization of MDA/MBSE features as follows: the computation independent model (CIM) is defined by the specification of the use-case model together with hybrid automata to gather the requirement analysis for control; the platform-independent model (PIM) is then designed by specializing the real-time UML/SysML’s features including main control capsules that depict structures and behaviors of controllers in detail; and the detailed PIM is subsequently converted into the platform-specific model (PSM) by object-oriented platforms to rapidly implement the AUV controller. Based on this proposed model, a horizontal planar trajectory-tracking controller was deployed and tested that permits a miniature AUV possessing a torpedo shape to reach and follow the desired horizontal planar trajectory.

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Zhao, Jing, Zhao Lin Han, and Yuan Yuan Fang. "Fuzzy Neural Network Hybrid Learning Control on AUV." Advanced Materials Research 468-471 (February 2012): 1732–35. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1732.

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A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

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Loh, Tzu Yang, Mario P. Brito, Neil Bose, Jingjing Xu, Natalia Nikolova, and Kiril Tenekedjiev. "A Hybrid Fuzzy System Dynamics Approach for Risk Analysis of AUV Operations." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (January 20, 2020): 26–39. http://dx.doi.org/10.20965/jaciii.2020.p0026.

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The maturing of autonomous technology has fostered a rapid expansion in the use of Autonomous Underwater Vehicles (AUVs). To prevent the loss of AUVs during deployments, existing risk analysis approaches tend to focus on technicalities, historical data and experts’ opinion for probability quantification. However, data may not always be available and the complex interrelationships between risk factors are often neglected due to uncertainties. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. The approach utilises the strengths while overcoming limitations of both system dynamics and fuzzy set theory. Presented as a three-step iterative framework, the approach was applied on a case study to examine the impact of crew operating experience on the risk of AUV loss. Results showed not only that initial experience of the team affects the risk of loss, but any loss of experience in earlier stages of the AUV program have a lesser impact as compared to later stages. A series of risk control policies were recommended based on the results. The case study demonstrated how the FuSDRA approach can be applied to inform human resource and risk management strategies, or broader application within the AUV domain and other complex technological systems.

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Macatangay, Xan, Reza Hoseinnezhad, Anthony Fowler, Sharmila Kayastha, and Alireza Bab-Hadiashar. "Addressing Actuator Saturation during Fault Compensation in Model-Based Underwater Vehicle Control." Electronics 12, no. 21 (November 1, 2023): 4495. http://dx.doi.org/10.3390/electronics12214495.

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Robust control systems are a necessity for autonomous underwater vehicle (AUV) systems due to the challenges they face during operation. Many AUV control-design methods have been developed for different actuator configurations, with robustness against model parameter uncertainties, environmental disturbances, and system faults. Actuator faults can reduce the physical capabilities of a system, which can be compensated for through control re-allocation. However, the increased control allocation to the remaining actuators may cause actuator saturation and reduce controller performance. In this work, we present a depth-pitch model-based nonlinear control law that directly considers actuator saturation, and a fault-tolerant control allocation method for a hybrid AUV actuator configuration. Two types of actuator faults are considered for an underwater vehicle with a hybrid actuator configuration. The proposed controller is implemented in a simulated system, and its trajectory tracking performance is compared with a baseline system without fault or saturation tolerance. To determine the utility of the proposed saturation and fault tolerance control methods, the tracking performance in these simulations is quantified in terms of the settling time, post-fault peak values, and root mean square of the depth and pitch errors.

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Zhang, Lei, Da Peng Jiang, Shu Ling Huang, and Jin Xin Zhao. "Research on Motion Control of AUV with Hybrid Actuators." Applied Mechanics and Materials 341-342 (July 2013): 906–12. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.906.

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A switch function is presented to smooth control instructions and avoid system vibration during switch operating underwater vehicles with hybrid actuators. And a Modified S-plane Controller (MSC) is proposed by analyzing underwater vehicles dynamics and taking static force and coupling effects between the longitude velocity and other dimensions into account. Besides the advantages of S controller such as simple structure, MSC can solve the motion control of underwater vehicle at high speed which is difficult to control with S-plane controller. The stability of MSC is analyzed with Lyapunov function. Finally, MSC is applied to the motion control of an autonomous underwater vehicle controlled by rudders and thrusters. The feasibility of MSC is demonstrated by the results of velocity control, yaw control and depth control tests.

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Zhao, Zhenyi, Qiao Hu, Haobo Feng, Xinglong Feng, and Wenbin Su. "A Cooperative Hunting Method for Multi-AUV Swarm in Underwater Weak Information Environment with Obstacles." Journal of Marine Science and Engineering 10, no. 9 (September 8, 2022): 1266. http://dx.doi.org/10.3390/jmse10091266.

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Cooperative hunting is a typical task that reflects the intelligence level of a swarm. For the complex underwater weak information environment with obstacles, a problem description of the multi-autonomous underwater vehicle (AUV) cooperative hunting task is given, considering the influencing factors, including underwater obstacles, AUV sensing interaction range, and target escape strategy. A hybrid adaptive preference method based on improved artificial potential fields (HAP-IAPF) is proposed. Then the strategies of obstacle avoidance and hunting are designed separately according to the task requirements. The adaptive weight control unit is used to adjust the preference strategy. The multi-AUV cooperative hunting in dynamic obstacle underwater environments under weakly connected conditions are achieved. In order to prove the effectiveness of the proposed algorithm, simulation results compared with the traditional artificial potential field method and the optimized artificial potential field method are given in this paper. The results show that the proposed method is robust and effective in different environments.

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Bingul, Zafer, and Kursad Gul. "Intelligent-PID with PD Feedforward Trajectory Tracking Control of an Autonomous Underwater Vehicle." Machines 11, no. 2 (February 17, 2023): 300. http://dx.doi.org/10.3390/machines11020300.

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This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is presented to guarantee stable and precise trajectory tracking performance in the complex underwater environment for AUVs. In the proposed hybrid controller, intelligent-PID (i-PID) and PD feedforward controllers are combined to achieve better disturbance rejections and initial tracking error compensations while keeping the trajectory tracking precision. A mathematical model of an AUV is derived, and ocean current dynamics are included to obtain better fidelity when examining ocean current effects. In order to evaluate the trajectory tracking control performance of the proposed controller, computer simulations are conducted on the LIVA AUV with a compelling trajectory under various disturbances. The results are compared with the two degrees-of-freedom (DOF) i-PID, i-PID, and PID controllers to examine control performance improvements with the guaranteed trajectory tracking stability. The comparative results revealed that the i-PID with PD feedforward controller provides an effective trajectory tracking control performance and excellent disturbance rejections for the entire trajectory of the AUV.

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Siregar, Simon, Bambang Riyanto Trilaksono, Egi Muhammad Idris Hidayat, Muljowidodo Kartidjo, Natsir Habibullah, Muhammad Fikri Zulkarnain, and Handi Nugroho Setiawan. "Design and Construction of Hybrid Autonomous Underwater Glider for Underwater Research." Robotics 12, no. 1 (January 5, 2023): 8. http://dx.doi.org/10.3390/robotics12010008.

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The main goal of this paper was to design and construct a hybrid autonomous underwater glider (HAUG) with a torpedo shape, a size of 230 cm in length and 24 cm in diameter. The control, navigation, and guidance system were executed simultaneously using a Udoo X86 minicomputer as the main server and three BeagleBone Black single-board computers as the clients. The simulations showed a controlled horizontal speed of 0.5 m/s in AUV mode and 0.39 to 0.51 m/s in glide mode with a pitch angle between 14.13∘ and 26.89∘. In addition, the field experiments under limited space showed the proposed HAUG had comparable results with the simulation, with a horizontal speed in AUV mode of 1 m/s and in glide mode of around 0.2 m/s. Moreover, the energy consumption with an assumption of three cycles of gliding motion per hour was 51.63 watts/h, which enabled the HAUG to perform a mission for 44.74 h. The proposed HAUG was designed to hold pressure up to 200 m under water and to perform underwater applications such as search and rescue, mapping, surveillance, monitoring, and maintenance.

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Cui, Yong, and Nilanjan Sarkar. "A unified force control approach to autonomous underwater manipulation." Robotica 19, no. 3 (April 25, 2001): 255–66. http://dx.doi.org/10.1017/s026357470000309x.

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A unified force control scheme for an autonomous underwater robotic system is proposed in this paper. This robotic system is composed of a six degree-of-freedom autonomous underwater vehicle (AUV) and a robotic arm that is mounted on the AUV. A unified force control approach, which combines impedance control with hybrid position/force control by means of fuzzy switching to perform autonomous underwater manipulation, is presented in this paper. This controller requires a dynamic model of the underwater vehicle-manipulator system. However, it does not require any model of the environment and therefore will have the potential to be useful in underwater tasks where the environment is generally unknown. The proposed approach combines the advantages of impedance control with hybrid control so that both smooth contact transition and force trajectory tracking can be achieved. In the absence of any functional autonomous underwater vehicle-manipulator system that can be used to verify the proposed controller, extensive computer simulations are performed and the results are presented in the paper.

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Wang, Xiaomin, Xiaohan Zhang, Zhou Zheng, and Xu Kong. "Hybrid coordination for the fast formation building of multi-small-AUV systems with the on-board cameras and limited communication." PeerJ Computer Science 9 (April 24, 2023): e1358. http://dx.doi.org/10.7717/peerj-cs.1358.

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Formation building for multi-small-AUV systems with on-board cameras is crucial under the limited communication underwater environment. A hybrid coordination strategy is proposed for the rapid convergence to a leader-follower pattern. The strategy consists of two parts: a time-optimal local-position-based controller (TOLC) and a distributed asynchronous discrete weighted consensus controller (ADWCC). The TOLC controller is designed to optimize the assignation of AUVs’ destinations in the given pattern and guide each AUV to its destination by the shortest feasible distance. The ADWCC controller is developed to direct the AUVs blocked by obstacles to reach their destinations with the information from the perceived neighbors by on-board cameras. The rapidity of the proposed strategy is theoretically discussed. The effectiveness of the proposed algorithm has been verified in the simulation environments in both MATLAB and Blender.

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Yuan, Jian, Feng Li Zhang, and Zhong Hai Zhou. "Finite-Time Formation Control for Autonomous Underwater Vehicles with Limited Speed and Communication Range." Applied Mechanics and Materials 511-512 (February 2014): 909–12. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.909.

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Cooperative control of multiple autonomous underwater vehicles (AUVs) plays an important role on marine scientific investigation and marine development. The formation of multi-AUV can significantly enhance applications on the marine sampling, imaging, surveillance and communications. Compared to the formation control of multi-robot, the formation control of multi-AUV is particularly difficult, especially on controlling attitude and direction of AUV; what is more, the communication method among AUVs is acoustic. When communication distance increases, the communication qualities deteriorate quickly; this mainly makes time-delay, signal attenuation and distortion. Although formation control of multiple AUVs obtains a wide range of attention in recent years, the fruits on formation control problem are less than ones on land multi-robot problems. For example, Fiorelli conducted a collaborative and adaptive sampling research of multi-AUV at the Monterey Bay [; Yu and Ura carried out the cable-based modular fast-moving and obstacle-avoidance experiments, and presented an interconnected multi-AUV system with three-dimension sensors. On the aspect of formation control framework [2-, [ proposed a four-layer cooperative control strategy based on hierarchical structure; [ proposed a hierarchical control framework based on hybrid model. In addition, Yang converted a nonholonomic system to a chain one and designed a controller to implement a leader-follower formation for multiple AUVs in [. The formation control for multiple autonomous underwater vehicles is rather different than the control methods for other vehicles, because the formation control for AUVs is of its characteristics, such as the large-scale distribution in space. The finite-time consensus controller designing based on finite-time control and consensus problem has important theoretical and practical significance. The decentralized controller methods for the autonomous underwater vehicle are applied more and more, but they ignore the coupling relationship between them. Another method is that an AUV is modeling as an agent, but this method ignores attitude characteristics of AUVs (pitch, roll and yaw). In this paper, we consider the cooperative control problem in three dimensional spaces. Finite-time formation for Autonomous Underwater Vehicles (AUVs) with constraints on communication range is investigated. We proposed a two-layer finite-time consensus control law, to avoid leading to collapse on formation because of failure leader, all AUVs are arrayed in the same level and each AUV can obtain global formation information. Finally, the simulation results show the effectiveness of the control strategy.

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Tran, Ngoc-Huy, and Thanh-Hai Chau. "Study on analysis and design of a VIAM- AUV2000 Autonomous Underwater Vehicle (AUV)." Science & Technology Development Journal - Engineering and Technology 3, SI1 (April 12, 2020): First. http://dx.doi.org/10.32508/stdjet.v3isi1.723.

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This paper presents the design of the VIAM-AUV2000 autonomous underwater vehicle (AUV) with a built-in cylinder for floatation and counterbalance. The modular structure, including mechanical design, electronic system, and control algorithm, ensures continuous operation for the vehicle at a depth of 50 meters underwater. The main content will consist of two parts: the mechanical implementation and the electrical system. The mechanical implementation part will focus on calculating ship hull profile and material selection; computing and simulating stress and distortion on ship hull and waterproof covering using finite element method with NX Nastran; analyzing and planning cylinder and counterbalance arrangements. At the same time, the advantages of hybrid AUV design inspired from the traditional one with thruster and fins, as well as the underactuated glider form using counterbalance and cylinder for diving and floating, are discussed specifically in the upcoming sections. The electrical system for the robot is also mentioned and clarified through the selection of sensors, actuators, and hardware design to ensure stable operation for the diving robot at a depth of 50m and operate continuously for long periods underwater by using traditional AUV mode and glider mode. Some experimental results of thruster and three-axis tilt estimators with an error of less than 1o are also presented in this paper.

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Lv, Peng-Fei, Bo He, and Jia Guo. "Position Correction Model Based on Gated Hybrid RNN for AUV Navigation." IEEE Transactions on Vehicular Technology 70, no. 6 (June 2021): 5648–57. http://dx.doi.org/10.1109/tvt.2021.3080134.

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Lodovisi, Chiara, Pierpaolo Loreti, Lorenzo Bracciale, and Silvello Betti. "Performance Analysis of Hybrid Optical–Acoustic AUV Swarms for Marine Monitoring." Future Internet 10, no. 7 (July 10, 2018): 65. http://dx.doi.org/10.3390/fi10070065.

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Матвиенко, Ю. В., В. В. Костенко, А. Ф. Щербатюк, and А. В. Ремезков. "DEVELOPMENT OF THE TECHNOLOGICAL POTENTIAL OF AUTONOMOUS UNDERWATER VEHICLES." Podvodnye issledovaniia i robototehnika, no. 4(34) (January 24, 2020): 4–14. http://dx.doi.org/10.37102/24094609.2020.34.4.001.

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Перспективы создания подводных роботов – автономных и телеуправляемых необитаемых подводных аппаратов (АНПА, ТНПА) нового поколения связаны с разработкой технологий гибридных необитаемых подводных аппаратов (ГНПА), в которых интегрированы функции АНПА и ТНПА. Проблема состоит в значительном расширении технологических возможностей АНПА за счет оснащения их новыми инструментами, совершенствования систем бортового управления, вовлечения оператора в технологический процесс для оперативного контроля хода работ, порядка и условий выполнения принципиально новых технологических операций. ГНПА может выполнять широкий спектр бесконтактных работ, выходить к объекту обследования в соответствии с заданной программой, выполнять действия, связанные с формированием канала информационного обмена с постом управления, и далее выполнять контактные операции в супервизорном режиме. Первоочередные задачи создания гибридных аппаратов состоят в развитии функциональных свойств АНПА, включая: интеллектуализацию системы бортового управления, организацию супервизорного управления с включением оператора в процесс управления, расширение инструментального оснащения и оптимизацию конструктивных решений, организацию сетевых средств подводной навигации, организацию инфраструктуры подводного базирования аппарата при выполнении длительных работ по обслуживанию подводных добычных комплексов. В работе показана принципиальная возможность создания ГНПА на основе робототехнических комплексов и их систем, созданных в ИПМТ ДВО РАН за последние годы. Prospects of designing autonomous and remotely operated underwater vehicles (AUV and ROV) of the new generation rely on developing hybrid autonomous and remotely-operated vehicles (ARV), which combine functions of AUV and ROV. It is necessary to expanse significantly AUV's technological capabilities by equipping it with new instruments, upgrading onboard control systems, involving operators in the technological process for real-time control of work progress, and execution order and conditions of novel technological operations. ARV is capable of performing a wide specter of contactless operations: approach the target of research according to the stated program, perform operations related to establishing a data exchange channel with control station, and then carry out contact operation in supervisory mode. Priority tasks of hybrid vehicles designing consist of the development of AUV's functional properties, including: intellectualization of onboard control systems; provision of supervisory control involving an operator in control flow; expansion of instrumentation and optimization of structural solutions; providing a network means for underwater navigation; providing an infrastructure for underwater deployment while carrying out long-term missions on the maintenance of underwater mining complexes. Work demonstrates a principal achievability of designing an ARV based on robotic complexes and their systems developed in the IMTP FEB RAS for the past years.

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Yu, Lin, Qinghao Meng, and Hongwei Zhang. "3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles." Journal of Marine Science and Engineering 9, no. 3 (March 10, 2021): 307. http://dx.doi.org/10.3390/jmse9030307.

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To achieve rapid and flexible vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle (MJ-AUV) with orthogonal joints was designed. This paper focuses on the 3-dimensional (3D) modeling and attitude control of the designed vehicle. Considering the situation of gravity and buoyancy imbalance, a 3D model of the MJ-AUV was established according to Newton’s second law and torque balance principle. And then the numerical simulation was carried out to verify the credibility of the model. To solve the problems that the pitch and yaw attitude of the MJ-AUV are coupled and the disturbance is unknown, a linear quadratic regulator (LQR) decoupling control method based on a linear extended state observer (LESO) was proposed. The system was decoupled into pitch and yaw subsystems, treated the internal forces and external disturbances of each subsystem as total disturbances, and estimated the total disturbances with LESO. The control law was divided into two parts. The first part was the total disturbance compensator, while the second part was the linear state feedback controller. The simulation results show that the overshoot of the controlled system in the dynamic process is nearly 0 rad, reaching the design value very smoothly. Moreover, when the controlled system is in a stable state, the control precision is within 0.005%.

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Luo, Hanjiang, Ziyang Xu, Jinglong Wang, Yuting Yang, Rukhsana Ruby, and Kaishun Wu. "Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network." Wireless Communications and Mobile Computing 2022 (May 2, 2022): 1–14. http://dx.doi.org/10.1155/2022/9471698.

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In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.

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Geranmehr, Behdad, and Kamran Vafaee. "Hybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term." International Journal of Maritime Technology 7 (March 1, 2017): 49–55. http://dx.doi.org/10.18869/acadpub.ijmt.7.49.

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Kiselev, N. K., and L. A. Martynova. "ALGORITHMS OF ELECTRIC NETWORK CONTROL OF A HYBRID POWER SUPPLY SYSTEM OF AUV." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 7 (March 1, 2022): 76–91. http://dx.doi.org/10.18522/2311-3103-2021-7-76-91.

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Châu, Thanh Hải, Trần Ngọc Huy, Tôn Thiện Phương, and Huỳnh Mạnh Diễn. "Building the controler for differential diving modes of VIAM-AUV2000." Science & Technology Development Journal - Engineering and Technology 3, SI2 (April 15, 2021): first. http://dx.doi.org/10.32508/stdjet.v3isi2.548.

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This paper presents a new form of the autonomous underwater vehicle with a built-in subsystem of cylinder and counterbalance to support floating and diving, the electrical and mechanical system is built-in a modular form for easy integration and disassembly - mounting and expanding the system. In particular, show optimal calculation for diving robot's profile, simulating stress, deformation impact on the profile of the robot to select the hull's material and thickness to make sure the robot is durable and steady at a depth of 50m. The paper also presents advantages of hybrid design between traditional AUV which uses propeller and rudder to turn and glider using counterbalance and cylinder to dive. In addition, the design of the control system for the robot is also mentioned and clarified through the selection of sensors, actuators, designing electrical circuit, designing 600W thruster and tri-axis rotation angles estimator for stable operation of the robot at a depth of 50m. In addition, the paper also presents the dynamic model of the diving robot VIAMAUV2000, from which, builds, simulates and applies controller for diving robot in two main forms: using thruster (AUV mode) and not using thruster (Glider mode).

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Cui, Zhiyu, Lu Liu, Boyu Zhu, Lichuan Zhang, Yang Yu, Zhexuan Zhao, Shiyuan Li, and Mingwei Liu. "Spiral Dive Control of Underactuated AUV Based on a Single-Input Fractional-Order Fuzzy Logic Controller." Fractal and Fractional 6, no. 9 (September 14, 2022): 519. http://dx.doi.org/10.3390/fractalfract6090519.

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Autonomous underwater vehicles (AUVs) have broad applications owing to their ability to undertake long voyages, strong concealment, high level of intelligence and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in the complex ocean environment and have attracted significant research attention. In this paper, we propose a single-input fractional-order fuzzy logic controller (SIFOFLC) as an AUV motion control system. First, a single-input fuzzy logic controller (SIFLC) was proposed based on the signed distance method, whose control input is the linear combination of the error signal and its derivative. The SIFLC offers a significant reduction in the controller design and calculation process. Then, a SIFOFLC was obtained with the derivative of the error signal extending to a fractional order and offering greater flexibility and adaptability. Finally, to verify the superiority of the proposed control algorithm, comparative numerical simulations in terms of spiral dive motion control were conducted. Meanwhile, the parameters of different controllers were optimized according to the hybrid particle swarm optimization (HPSO) algorithm. The simulation results illustrate the superior stability and transient performance of the proposed control algorithm.

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Mi, Zhen-Shu, Ahmad C. Bukhari, and Yong-Gi Kim. "An Obstacle Recognizing Mechanism for Autonomous Underwater Vehicles Powered by Fuzzy Domain Ontology and Support Vector Machine." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/676729.

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The autonomous underwater vehicle (AUV) and the problems associated with its safe navigation have been studied for the last two decades. The real-time underwater obstacle recognition procedure still has many complications associated with it and the issue becomes worse with vague sensor data. These problems can be coped with the merger of a robust classification mechanism and a domain knowledge acquisition technique. In this paper, we introduce a hybrid mechanism to recognize underwater obstacles for AUV based on fuzzy domain ontology and support vector machine (SVM). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years and is a new generation learning system based on recent advances in statistical learning theory. The amalgamation of fuzzy domain ontology with SVM boosts the performance of the obstacle recognition module by providing the timely semantic domain information of the surrounding circumstances. Also the reasoning ability of the fuzzy domain ontology can expedite the obstacle avoidance process. In order to evaluate the performance of the system, we developed a prototype simulator based on OpenGL and VC++. We compared the outcomes of our proposed technique with backpropagation algorithm and classic SVM based techniques.

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Wang, Xuehao, Yanhui Wang, Peng Wang, Shaoqiong Yang, Wendong Niu, and Yehao Yang. "Design, analysis, and testing of Petrel acoustic autonomous underwater vehicle for marine monitoring." Physics of Fluids 34, no. 3 (March 2022): 037115. http://dx.doi.org/10.1063/5.0083951.

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Autonomous underwater vehicles (AUVs) provide a unique and cost-effective platform for conducting ocean acoustic measurements and surveys such as marine mammal monitoring, extensive area seafloor topography survey, and fish stock survey. The small and medium-sized AUVs combine load-carrying capability, motion, and range and allow for timescale observations that are difficult to achieve with traditional acoustic observation platforms. However, self-noise is still an unavoidable challenge for AUVs when applied in passive acoustic monitoring. This paper presents the acoustic observation application of the Petrel acoustic AUV in marine monitoring. The layout design of a Petrel acoustic AUV and acoustic sensor integration are described in detail, and the self-noise characteristics of Petrel acoustic AUVs are evaluated by simulation and testing. The results showed that the self-noise level of Petrel acoustic AUVs is effectively controlled with its optimized design, and the maximum noise of the propulsion system is only 75 dB in the low-frequency band. Therefore, the Petrel acoustic AUV can be used as an ideal acoustic observation platform to carry out diverse observation tasks. Finally, a solution of fixed-depth motion is proposed to address the problem that frequent attitude adjustment affects the quality of acoustic data in fixed-depth navigation. According to a sea trial in the South China Sea, the method can significantly reduce the attitude adjustment frequency and, thus, provide an ideal environment for acoustic observation. This method also applies to other underwater mobile observation platforms. This study provides a reference for the acoustic integration design of underwater equipment such as long-range AUVs or hybrid-driven underwater gliders.

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Wang, Biao, Chao Wu, and Tong Ge. "Self-Repairing Control System for a Hybrid Underwater Vehicle." Advanced Materials Research 834-836 (October 2013): 1256–62. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1256.

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A novel remotely operated underwater vehicle-a hybrid remotely operated underwater vehicle (HROV) capable of working to the full ocean depth has been developed. The battery powered vehicle operates in two modes. For broad-area survey, the vehicle can operate as an autonomous underwater vehicle (AUV) capable of mapping the sea floor with sonars and cameras. For close up imaging and sampling, the vehicle can operate as a remotely operated underwater vehicle (ROV) employing a optic fiber tether for real-time telemetry of data and video to its operators on a surface ship. In order for the vehicle to achieve a certain survivability and reliability level, a self-repairing control system (SRCS) has been designed. This paper presents the two basic technologies in SRCS: fault diagnosis and isolation (FDI) and reconfigurable control. For FDI, a model-based hierarchical fault diagnosis system is designed for the HROV. Then, control strategies which reconfigure the control system at intervals according to information from the FDI system are presented. Combining the two technologies, we obtained the fundamental frame of SRCS for the HROV.

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Ridao, P., M. Carreras, J. Batlle, and J. Amat. "O 2 CA 2 : A New Hybrid Control Architecture for a Low Cost AUV." IFAC Proceedings Volumes 34, no. 7 (July 2001): 311–16. http://dx.doi.org/10.1016/s1474-6670(17)35101-7.

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Das, S. K., S. N. Shome, S. Nandy, and D. Pal. "Modeling a hybrid reactive-deliberative architecture towards realizing overall dynamic behavior of an AUV." Procedia Computer Science 1, no. 1 (May 2010): 259–68. http://dx.doi.org/10.1016/j.procs.2010.04.029.

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Zhang, Ru Bo, Hai Bo Tong, and Chang Ting Shi. "A Control Architecture for Mission Re-Planning and Plan Repair of AUV." Applied Mechanics and Materials 462-463 (November 2013): 794–97. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.794.

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This paper present a hybrid, hierarchical control architecture for mission re-planning and plan repair of autonomous underwater vehicle (AUV) navigating in dynamic and uncertain marine environment. The proposal carries out a component-oriented part-based control architecture structured in three parts: situation reasoning, re-planning trigger and hierarchical re-planning layer. Situation reasoning using the unstructured real-word information obtained by sorts of sensor detectes and recognizes uncertain event. According the event types and influence degree, the re-planning trigger decides the re-planning level. Hierarchical re-planning layer contains mission re-planning, task re-planning and behavior re-planning. Different re-planning level depends on the result of re-planning trigger. Preliminary versions of the architecture have been integrated and tested in a marine simulation environment.

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Yu, Xue, Wei-Neng Chen, Xiao-Min Hu, Tianlong Gu, Huaqiang Yuan, Yuren Zhou, and Jun Zhang. "Path Planning in Multiple-AUV Systems for Difficult Target Traveling Missions: A Hybrid Metaheuristic Approach." IEEE Transactions on Cognitive and Developmental Systems 12, no. 3 (September 2020): 561–74. http://dx.doi.org/10.1109/tcds.2019.2944945.

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Xuyu, Shen, Sun Gongwu, Mao Ying, Hu Xuanyu, and Chu Zhenzhong. "Gaussian Process-Based Model Predictive Control for Autonomous Underwater Vehicles." Journal of Physics: Conference Series 2718, no. 1 (March 1, 2024): 012063. http://dx.doi.org/10.1088/1742-6596/2718/1/012063.

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Abstract Traditional MPC algorithms, assuming constant values, suffer from performance degradation caused by model mismatch. This paper addresses the enhancement of predictability in Autonomous Underwater Vehicles (AUVs) under uncertain disturbances and unknown system dynamics through the design of Model Predictive Controllers (MPCs). We propose a hybrid model that integrates a first-principles nominal model with a learning-based model utilizing Gaussian Processes (GPs). The algorithm addresses the problem of model mismatch in AUV motion control by constructing a precise GP model, which captures the dynamic characteristics of the process through the collection and learning of deviations between the reference model and the controlled system. Additionally, the GP model transforms stochastic constraints into deterministic convex constraints, enhancing safety guarantees in complex and challenging environments. The effectiveness of the proposed algorithm is demonstrated through two simulation examples.

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Zeng, Zhenfang, Wei Yue, and Lei Zhu. "Finite-time fuzzy cooperative control for multi-AUV systems under cyber-attacks with hybrid unknown nonlinearities." Ocean Engineering 304 (July 2024): 117875. http://dx.doi.org/10.1016/j.oceaneng.2024.117875.

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Galushko, I. D., V. A. Salmina, and G. M. Makaryants. "DEVELOPMENT OF AN APPROACH TO THE MANAGEMENT OF THE STRUCTURE OF WALL-FLOWING." Journal of Dynamics and Vibroacoustics 5, no. 4 (March 12, 2020): 13–20. http://dx.doi.org/10.18287/2409-4579-2019-5-4-13-20.

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The development of a prototype of an underwater robot with a variable geometry of the body is a major complex task, including a whole series of tasks of a scientific, technical, engineering and applied nature, and the creation of new automatic systems for managing it. The creation of such a robot will ensure its high autonomy and maneuverability. In this paper, the development of a solid model of a biomimetic robot hybrid and AUV with a variable geometry of the hull is presented. Also presented is a basic pneumatic circuit integrated into the robot body, which drives the pneumatic muscles fixed to the ribs of the robot's robust body. The experimental part includes the investigation of the vertical and horizontal components of the deformation of the walls of the shell, depending on the pressure supplied by the pneumatic system to the pneumatic muscles.

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Wang, Dianrui, Yue Shen, Junhe Wan, Qixin Sha, Guangliang Li, Guanzhong Chen, and Bo He. "Sliding mode heading control for AUV based on continuous hybrid model-free and model-based reinforcement learning." Applied Ocean Research 118 (January 2022): 102960. http://dx.doi.org/10.1016/j.apor.2021.102960.

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Zhang, Wei, Naixin Wang, and Wenhua Wu. "A hybrid path planning algorithm considering AUV dynamic constraints based on improved A* algorithm and APF algorithm." Ocean Engineering 285 (October 2023): 115333. http://dx.doi.org/10.1016/j.oceaneng.2023.115333.

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Salhaoui, Marouane, J. Carlos Molina-Molina, Antonio Guerrero-González, Mounir Arioua, and Francisco J. Ortiz. "Autonomous Underwater Monitoring System for Detecting Life on the Seabed by Means of Computer Vision Cloud Services." Remote Sensing 12, no. 12 (June 19, 2020): 1981. http://dx.doi.org/10.3390/rs12121981.

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Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements.

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Zhang, Yiqiang, Jiaxing Che, Yijun Hu, Jiankuo Cui, and Junhong Cui. "Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach." Sensors 23, no. 14 (July 14, 2023): 6417. http://dx.doi.org/10.3390/s23146417.

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Autonomous underwater vehicles (AUVs) may deviate from their predetermined trajectory in underwater currents due to the complex effects of hydrodynamics on their maneuverability. Model-based control methods are commonly employed to address this problem, but they suffer from issues related to the time-variability of parameters and the inaccuracy of mathematical models. To improve these, a meta-learning and self-adaptation hybrid approach is proposed in this paper to enable an underwater robot to adapt to ocean currents. Instead of using a traditional complex mathematical model, a deep neural network (DNN) serving as the basis function is trained to learn a high-order hydrodynamic model offline; then, a set of linear coefficients is adjusted dynamically by an adaptive law online. By conjoining these two strategies for real-time thrust compensation, the proposed method leverages the potent representational capacity of DNN along with the rapid response of adaptive control. This combination achieves a significant enhancement in tracking performance compared to alternative controllers, as observed in simulations. These findings substantiate that the AUV can adeptly adapt to new speeds of ocean currents.

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Eickstedt, Donald P., and Scott R. Sideleau. "The Backseat Control Architecture for Autonomous Robotic Vehicles: A Case Study with the Iver2 AUV." Marine Technology Society Journal 44, no. 4 (July 1, 2010): 42–54. http://dx.doi.org/10.4031/mtsj.44.4.1.

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Abstract In this paper, an innovative hybrid control architecture for real-time control of autonomous robotic vehicles is described as well as its implementation on a commercially available autonomous underwater vehicle (AUV). This architecture has two major components, a behavior-based intelligent autonomous controller and an interface to a classical dynamic controller that is responsible for real-time dynamic control of the vehicle given the decisions of the intelligent controller over the decision state space (e.g., vehicle course, speed, and depth). The driving force behind the development of this architecture was a desire to make autonomy software development for underwater vehicles independent from the dynamic control specifics of any given vehicle. The resulting software portability allows significant code reuse and frees autonomy software developers from being tied to a particular vehicle manufacturer’s autonomy software and support as long as the vehicle supports the required interface between the intelligent controller and the dynamic controller. This paper will describe in detail the components of the backseat driver architecture as implemented on the Iver2 underwater vehicle, provide several examples of its use, and discuss the future direction of the architecture.

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Bowen, Andrew D., Dana R. Yoerger, Louis L. Whitcomb, and Daniel J. Fornari. "Exploring the Deepest Depths: Preliminary Design of a Novel Light-Tethered Hybrid ROV for Global Science in Extreme Environments." Marine Technology Society Journal 38, no. 2 (June 1, 2004): 92–101. http://dx.doi.org/10.4031/002533204787522776.

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This paper describes a new project to build an operational underwater vehicle that can perform scientific survey and sampling to the full depth of the ocean (11,000 meters). The vehicle, called a hybrid remotely operated vehicle (HROV), will operate in two different modes. For broad area survey, the vehicle will operate untethered as a autonomous underwater vehicle (AUV) capable of exploring and mapping the seafloor with sonars and cameras. After targets of interest have been found, the vehicle will be converted at-sea to become a remotely operated vehicle (ROV) that will enable close up imaging and sampling. The ROV configuration will incorporate a lightweight fiber optic tether to the surface for high bandwidth real-time video and data telemetry to the surface to enable high-quality teleoperation, additional cameras and lights, a manipulator arm, and sampling gear. This paper outlines the scientific motivation for the project as well as the feasibility of our design concept. Analysis of the fiber optic cable shows our approach to be practical even with fairly extreme current profiles. An overall approach to the vehicle design is also presented, including options for pressure housings and buoyancy materials.

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Zhu, Jiupeng, An Li, Fangjun Qin, Hao Che, and Jungang Wang. "A Novel Hybrid Method Based on Deep Learning for an Integrated Navigation System during DVL Signal Failure." Electronics 11, no. 19 (September 20, 2022): 2980. http://dx.doi.org/10.3390/electronics11192980.

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The navigation performance of an autonomous underwater vehicle (AUV) as the main tool for exploring the ocean greatly affects its work efficiency. Under the circumstance that high-precision GNSS positioning signals cannot be obtained, the role of the Strapdown Inertial Navigation System/Doppler Velocity Log (SINS/DVL) integrated navigation system is becoming more prominent. Due to marine creatures or the seafloor topography, DVL is prone to outliers or even failures during measurement. To solve these problems, a LSTM/SVR-VBAKF algorithm aided integrated navigation system is proposed. First, under normal circumstances of DVL, the output information of SINS and DVL are used as training samples, and they train the Long Short-Term Memory (LSTM) model. To enhance the robustness and adaptability of the filter, a novel variational Bayesian adaptive filtering algorithm based on support vector regression is proposed. When the DVL formation is missing, the deep learning method adopted in this paper will be continuously output to ensure the effect of integrated navigation. The shipboard test data is verified from two aspects: filter performance and neural network-assisted integrated navigation system capability. The experimental results show that the new method proposed in this paper can effectively handle a situation where DVL output is not available.

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Journal articles on the topic 'Hybrid AUV'

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