Search Results (9)

Three-dimensional (3D) path planning for Unmanned Underwater Vehicles (UUVs) in typical marine operating conditions presents high-dimensional, non-convex optimization challenges due to undulating seabed topography, underwater threat sources, and coupled multi-physical constraints. Existing studies lack multi-strategy collaborative optimization mechanisms specifically designed for UUV 3D marine navigation constraints, thereby hindering the simultaneous achievement of real-time performance, safety, and energy efficiency in path planning. This paper first develops a comprehensive multi-dimensional cost function based on the dynamic characteristics of UUV underwater 3D navigation, operational rules for typical marine operating conditions, and safe navigation requirements through mathematical modeling, thereby formally transforming the UUV 3D path planning problem in typical marine operating conditions into a multi-constrained nonlinear global optimization problem. To address this challenge, an Adaptive Cooperative WMA (ACWMA) is proposed. The key improvements include: (i) an adaptive parameter switching and Lévy flight disturbance mechanism to balance exploration and exploitation capabilities; (ii) an optimal value leadership strategy to accelerate convergence; and (iii) a team collaborative learning mechanism to enhance population optimization efficiency. Algorithm benchmark performance is validated using the CEC 2017 standard test suite, while comparative and ablation experiments are conducted in multi-gradient complex marine 3D scenarios. The statistical significance of the algorithm performance improvement is verified using the Wilcoxon rank-sum test. The proposed ACWMA achieves a significant performance improvement of 8.71% over the suboptimal WMA in terms of core performance metrics and generates low-energy-consumption 3D paths that satisfy multiple constraints. These findings provide valuable engineering insights for 3D path planning in UUV autonomous operations within typical marine operating conditions. Full article

Researchers are currently conducting several studies in the field of navigation systems and sensors. Even in the past, there was a lot of research regarding the field of velocity sensors for unmanned underwater vehicles (UUVs). UUVs have various services and significance in the military, scientific research, and many commercial applications due to their autonomy mechanism. So, it’s very crucial for the proper maintenance of the navigation system. Reliable navigation of unmanned underwater vehicles depends on the quality of their state determination. There are so many navigation systems available, like position determination, depth information, etc. Among them, velocity determination is now one of the most important navigational criteria for UUVs. The key source of navigational aids for different deep-sea research projects is water currents. These days, many different sensors are available to monitor the UUV’s velocity. In recent times, there have been five primary types of sensors utilized for UUV velocity forecasts. These include Doppler Velocity Logger sensors, paddlewheel sensors, optical sensors, electromagnetic sensors, and ultrasonic sensors. The most popular sensing sensor for estimating velocity at the moment is the Doppler Velocity Logger (DVL) sensor. DVL sensor is the most fully developed sensor for UUVs in recent years. In this work, we offer an overview of the field of navigation systems and sensors (especially velocity) developed for UUVs with respect to their use with tidal current sensing in the UUV setting, including their history, evolution, current research initiatives, and anticipated future. Full article

With the increasing complexity of ocean missions, using multiple unmanned underwater vehicles to collaborate in executing tasks has become an effective way to improve the overall efficiency of ocean operations. Current research on path planning for multiple unmanned underwater vehicles mainly focuses on the basis of particle models or fully known environmental information, while research directions mainly focus on single indicators such as completion time and energy consumption. This paper first constructs a UUV model and a task scenario with detection success rate as the objective function. Then, a parameterization method based on a spiral search path was proposed for designing variables. A hierarchical control strategy is designed to ensure handle formation constraints. A general optimization framework for task scenarios has been constructed and combined with algorithms to solve optimization problems. Finally, this study compared and analyzed the performance of different optimization algorithms under the optimization framework, evaluated the optimization results of different search strategies, and explored the impact of dynamic objectives on the detection success rate. The results showed that the optimized path had a search success rate that increased by more than 50% compared to the direct path and the cover search path, which verified the effectiveness of the proposed method and strategy. Full article

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment. Full article

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV’s flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions. Full article

The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio. Full article

Efficiency has always been one of the most critical indicators for evaluating wireless power transfer (WPT) systems. To achieve fast maximum energy efficiency tracking (MEET), this paper provides an innovative control method utilizing the hill climbing-golden section search (HC-GSS) method of an LCC-S compensated WPT system. The receiver side includes a buck-boost converter that regulates the output current or voltage to meet output requirements. In the meantime, the buck-boost converter on the transmitter side is managed by the HC-GSS approach for MEET by minimizing the input power under the premise of output stability. Compared with the conventional P&O method, the HC-GSS method can eliminate the trade-off between the oscillation and convergence rate because it is designed for different system stages. In this WPT system, there is no need for direct communication between the transmitter and receiver. Therefore, the system is potentially cheaper to implement and does not suffer from annoying communication delays, which are prevalent in underwater environments for unmanned underwater vehicles’ (UUV) WPT systems. Both the simulation and experiment results show that this method can improve the efficiency of the WPT system without communication. The proposed method remains valid with coupler displacement as it does not include the mutual inductance of the system. Full article

Unmanned underwater vehicles (UUVs) are key technologies to conduct preventive inspection and maintenance tasks in offshore renewable energy plants. Making such vehicles autonomous would lead to benefits such as improved availability, cost reduction and carbon emission minimization. However, some technological aspects, including the powering of these devices, remain with a long way to go. In this context, underwater wireless power transfer (UWPT) solutions have potential to overcome UUV powering drawbacks. Considering the relevance of this topic for offshore renewable plants, this work aims to provide a comprehensive summary of the state of the art regarding UPWT technologies. A technology intelligence study is conducted by means of a bibliographical survey. Regarding underwater wireless power transfer, the main methods are reviewed, and it is concluded that inductive wireless power transfer (IWPT) technologies have the most potential. These inductive systems are described, and their challenges in underwater environments are presented. A review of the underwater IWPT experiments and applications is conducted, and innovative solutions are listed. Achieving efficient and reliable UWPT technologies is not trivial, but significant progress is identified. Generally, the latest solutions exhibit efficiencies between 88% and 93% in laboratory settings, with power ratings reaching up to 1–3 kW. Based on the assessment, a power transfer within the range of 1 kW appears to be feasible and may be sufficient to operate small UUVs. However, work-class UUVs require at least a tenfold power increase. Thus, although UPWT has advanced significantly, further research is required to industrially establish these technologies. Full article

Ducted propeller is a kind of special propeller widely used in unmanned underwater vehicles, its flow characteristics and hydrodynamic noise are very important for marine environmental protection and equipment concealment. The hybrid techniques based on the acoustic analogy theory are adopted in the present study to calculate the unsteady flow field and sound field characteristics of a ducted propeller. The full scale flow filed and hydro-acoustic sources of the propulsion system are simulated by Detached-Eddy computational fluid dynamics (CFD) method. Hydrodynamic noise are calculated by FWH equation based on the CFD results. The frequency domain and directivity of sound pressure level at different sound field monitoring points are analyzed at four navigational speeds. The results show that the navigational speed that is in the inflow condition of the ducted propeller play important roles in the flow structure and underwater radiated noise. Under the fixed impeller rotational speed, the propulsion efficiency of ducted propeller increases first and then decreases with the raise of navigational speed. The maximum errors of thrust and power between simulation and experiment values are 0.5% and 0.1% respectively, which means that the adopted DES numerical simulation method has high credibility in calculating the acoustic source. At impeller rotational speed of 2000 r/min, the best state of flow field distribution is at the navigational speed of 1.54 m/s, which is corresponding to the highest propulsion efficiency condition. The propeller noise presents dipole characteristic in all working conditions, and at the obvious blade passing frequency, multiple characteristics are presented; most of the noise contribution is also concentrated below four times of the blade passing frequency. The total sound pressure level of the hydrodynamic noise is the smallest at the optimal efficiency condition (the navigational speed is 1.54 m/s). At high navigational speed, the low frequency characteristics below blade passing frequency increase and the amplitude becomes larger. This indicates that the component of turbulent noise becomes more important with the increase of navigational speed. The research focuses on analyzing the relationship between the energy loss of the ducted propeller wake field and the noise level, and it is found that the vortex at the tail makes a certain contribution to the noise. The research conclusions could provide some reference for the acoustic performance evaluation and noise reduction optimization of ducted propeller design as well as the improvement of UUV stealth performance. Full article