Unmanned Underwater Vehicles Research Articles

Batch Estimation of a Steady, Uniform, Flow-Field from Ground Velocity and Heading Measurements

This paper presents three batch estimation methods that use noisy ground velocity and heading measurements from a vehicle executing a circular orbit (or similar large heading change maneuver) to estimate the speed and direction of a steady, uniform, flow-field. The methods are based on a simple kinematic model of the vehicle's motion and use curve-fitting or nonlinear least-square optimization. A Monte Carlo simulation with randomized flow conditions is used to evaluate the batch estimation methods while varying the measurement noise of the data and the interval of unique heading traversed during the maneuver. The methods are also compared using experimental data obtained with a Bluefin-21 unmanned underwater vehicle performing a series of circular orbit maneuvers over a five hour period in a tide-driven flow.

Three-Dimensional Obstacle Avoidance and Path Planning for Unmanned Underwater Vehicles Using Elastic Bands

Three-Dimensional Obstacle Avoidance and Path Planning for Unmanned Underwater Vehicles Using Elastic Bands

Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning.

Water cooling structure design and temperature field analysis of permanent magnet synchronous motor for underwater unmanned vehicle

Unmanned underwater vehicles (UUVs) have extremely harsh cooling conditions for permanent magnet synchronous motor (PMSM) due to global sealing and compact space, and an efficient heat dissipation system is essential for the safety and operational reliability of PMSM. Currently, with the introduction of seawater and the addition of heat exchanger, liquid reservoir are typically used for open-circuit heat dissipation, which not only adds weight, but seriously affects the flow characteristics of the UUV. In this paper, a novel dual channel water cooling structure is designed to achieve internal circulating heat dissipation without additional auxiliary devices. The temperature field of the PMSM with different water cooling structures is solved by Computational Fluid Dynamics (CFD) under rated operating conditions. The results show that the cooling efficiency of water cooling structures is more affected by low flow rates, accompanied by increasing the flow rate from 0.5 L/min to 24 L/min, the cooling performance is enhanced, but does not entirely obey a strict linear variation. Furthermore, at a flow rate of 6 L/min, the dual spiral water cooling structure performs the best heat dissipation, the maximum operating temperature of the PMSM is reduced by 51.72 K, the maximum temperature rise suppression rate reaches 53.02 %, and the minimum differential pressure of 10.18 kPa.

Mask-guided deep learning fishing net detection and recognition based on underwater range gated laser imaging

In this paper, a mask-guided deep learning fishing net detection and recognition method based on underwater range gated laser imaging is proposed. Range gated laser imaging technology is used to obtain high quality underwater fishing net images with less water backscattering effect and background noise. A dual-phase training strategy including mask-guided feature extraction phase and classification finetune phase is proposed to avoid overfitting of training the neural network. The mask-guided feature extraction phase takes advantages of image segmentation training from synthetic dataset to get a better feature extraction performance. The highest overall accuracy of the proposed method reaches 95.49% in fishing net classification task under finetuned weight configuration. The proposed method can effectively help unmanned underwater vehicles and robots from entangling by fishing nets as well as retrieving derelict fishing nets for marine environment protection.

Special Issue: Machine Learning and Representation Issues in CAD/CAM

Abstract Machine Learning (ML), a sub-field of Artificial Intelligence (AI), is profoundly reshaping various aspects of human life. Its application in engineering systems promises to address long-standing challenges, although it also introduces new questions. While the potential of ML is undeniable, integrating existing ML methods into Computer-Aided Design and Manufacturing (CAD/CAM) presents distinctive challenges. These challenges encompass representation, adaptation, and the development of novel ML techniques to enhance CAD/CAM systems for diverse design and manufacturing solutions. This special issue aims to explore and resolve issues related to effective engineering model representation for ML, the integration of neural networks and deep generative models into CAD/CAM, mathematical frameworks combining ML with CAD/CAM in geometry and topology, data interpretation, and physics-based learning. The issue features ten papers that delve into various topics, including material prediction for assemblies, robotics mechanism design, multi-modal ML in engineering design, data-driven component segmentation in engineering drawings, evaluating assembly-part semantic knowledge in language models, three-dimensional slice reconstruction from high-resolution 2D images, physics-informed neural networks to expedite thermal simulations in additive manufacturing, transfer learning for defect detection in steel strips, AI-aided hull form design for energy-efficient unmanned underwater vehicles, and real-time high-precision calibration of quadruped robots using machine vision and artificial neural networks. Below, we provide concise summaries of each of the ten papers published in this special issue.

Incentive Oriented Two-Tier Task Offloading Scheme in Marine Edge Computing Networks: A Hybrid Stackelberg-Auction Game Approach

With the increasing exploration of marine resources, various marine wireless devices have been rapidly deployed for different marine applications such as marine navigation, ocean environment monitoring, and seabed resource exploitation. However, due to long transmission delay and low data rate between marine wireless devices and the cloud, it is challenging to satisfy the service requirements of computing-intensive and delay-sensitive tasks. By migrating computing resources from cloud to the near side of ocean, the paradigm of marine edge computing networks, which integrates communication and computation capacities in marine wireless devices, is expected to support a variety of marine tasks (e.g., data collection, monitoring and processing) with low delay and high data rate. However, considering the rationality and selfishness of marine wireless devices and their limited computing-capacity, how to motivate marine wireless devices to conduct task processing becomes an important problem for improving computing efficiency. To address this issue, in this paper, we propose an incentive oriented two-tier task offloading scheme for marine edge computing networks via hybrid Stackelberg-auction game approach, with the objective of improving the offloading efficiency and maximizing marine wireless devices’ utilities. Specifically, for underwater acoustic transmission tier, we exploit multi-access task offloading scheme, in which underwater wireless sensor (UWS) uploads its workloads to an unmanned underwater vehicle (UUV) and a sea surface sink node (SN) via non-orthogonal multiple access (NOMA) transmission. We formulate the utility of each party and model the task offloading process among UWS, UUV and SN as a Stackelberg game to optimize the UWS’s offloading strategy, UUV’s and SN’s price strategies. For radio frequency transmission tier, SN can offload its partial workloads to an unmanned aerial vehicle (UAV) via frequency division multiple access (FDMA) transmission. We provide their utilities and model the offloading process between a SN and a UAV as a double auction game to optimize their bidding strategies. Extensive simulation results are provided to validate the performance of the proposed scheme. Numerical results demonstrate that the proposed algorithms can obtain the optimal solutions and increase the utilities for marine wireless devices.

Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.

Depth control of an unmanned underwater vehicle based on robust inversion and sliding mode variable structure control

This paper addresses the depth control problem of an underactuated Unmanned Underwater Vehicle (UUV) subject to model uncertainties and ocean current interference. A robust inversion sliding mode variable structure control method is proposed. First, a vertical-plane UUV depth control model is established. Then, the inversion control method is combined with the sliding mode variable structure control method to devise an inversion sliding mode control algorithm. In order to further improve the control system’s robustness against external environmental interference, a robust control methodology is introduced for the design of the depth controller. Simulation results demonstrate that the designed controller effectively addresses the issues of model uncertainties and ocean current interference, thereby enhancing the UUV’s depth control accuracy.

Unmanned Underwater Vehicle Autonomy and Control near Submarines Using Actively Sampled Surrogates

Many tools have been developed to simulate unmanned underwater vehicle (UUV) motion and autonomous behaviors to evaluate UUV capabilities. However, there is no simulator that performs real-time modeling of the complex hydrodynamic interaction forces that a UUV experiences when operating near a moving submarine. These hydrodynamic interactions must be determined in real time to simulate the launch and recovery of UUVs from submarines. Potential flow models may be fast enough to solve the hydrodynamic interactions in real time, but by oversimplifying the physics and neglecting viscosity, they introduce inaccuracies into the simulations. Computational fluid dynamics (CFD) is capable of accurately modeling these hydrodynamic interactions, but simulations take hours or days to solve. To overcome this obstacle, a machine learning method known as Gaussian process (GP) regression is used to create a surrogate reduced-order-model that predicts the hydrodynamic interactions in real time. The GP regression model is trained by actively sampling CFD simulations in order to accurately model complex hydrodynamic interactions. This new approach allows the GP regression model to be incorporated into a UUV motion simulator and evaluate how the UUV is affected by the hydrodynamic interactions. Operating envelopes are developed that outline regions where the UUV safely overcomes the hydrodynamic interactions and where the UUV is overpowered and collides with the submarine. By incorporating this surrogate model into the autonomy architecture, new autonomous behaviors are created that compensate for the hydrodynamic interactions by adjusting the desired UUV heading and speed which allows it to better stay on course.

Modern electronics component base for building automated electronic systems with artificial intelligence

The article provides an overview of the prospects for the use of nanomaterials for solving modern tasks in robotics. A practical analysis of materials at the stage of designing structural elements as part of the development of a low-noise underwater unmanned aerial vehicle with artificial intelligence for reconnaissance and destruction of surface and underwater objects is demonstrated. The prospects of using nanotechnology to improve the characteristics of quantum converters are shown. An increase in sensitivity and an expansion of the spectral range of optical sensors have been established. The possibility of using such sensors in practical designs of robotics is shown

Budget and Management Inspiration for the Research and Development Expenses of the US Navy's Unmanned Underwater Vehicle

Budget and Management Inspiration for the Research and Development Expenses of the US Navy's Unmanned Underwater Vehicle

A Trajectory Tracking and Local Path Planning Control Strategy for Unmanned Underwater Vehicles

The control strategy of an underdriven unmanned underwater vehicle (UUV) equipped with front sonar and actuator faults in a continuous task environment is investigated. Considering trajectory tracking and local path planning in complex-obstacle environments, we propose a task transition strategy under the event-triggered mechanism and design the corresponding state space and action space for the trajectory tracking task under the deep reinforcement learning framework. Meanwhile, a feed-forward compensation mechanism is designed to counteract the effects of external disturbances and actuator faults in combination with a reduced-order extended state observer. For the path planning task under the rapidly exploring random tree (RRT) framework, a reward component and angular factors are introduced to optimize the growth and exploration points of the extended tree under the consideration of the shortest distance, optimal energy consumption, and steering angle constraints. The effectiveness of the proposed method was verified through continuous task simulations of trajectory tracking and local path planning.

Level and Program Analytics of MUM-T System

This study establishes the concept and classification system of MUM-T for the operation and development of AI-based complex combat systems. We analyze the core aspects of this system: autonomy, interoperability, and program level. AI MUM-T can improve the survivability of manned systems, expand their operational range, and increase combat effectiveness. We analyze technical challenges and program levels using data from the USA and UK, which are building the AI MUM-T integrated combat system. Currently, MUM-T is at the level of complex operation of a manned platform and an unmanned aerial vehicle platform. In the mid to long term, interoperable communication between heterogeneous platforms such as unmanned ground vehicles, unmanned surface vehicles, and unmanned underwater vehicles is possible. Depending on the level of development of the common architecture and standard protocols for interoperability between AI MUM-T systems, MUM-T can evolve from the “1 to N” concept to various combinations of operating concepts from “N to N.” The difference of this study from existing studies is that the core technologies of the fourth industrial revolution, such as AI, autonomy, and data interoperability, are reflected in the MUM-T system. In addition, an AI-enabled autonomous MUM-T operation and facility classification system was established by reflecting AI and autonomy in the existing unmanned system taxonomy, and the level and program were analyzed taking this into consideration.

MURA: a Multipurpose Underwater Robotic Arm mounted on Kalypso UUV in aquaculture

Underwater vehicles utilized in net cages at aquaculture facilities are commonly utilized for the purpose of examining the deterioration of nets and the accumulation of biofouling. The implementation of a robotic system for repairing damages has the potential to decrease the expenses associated with employing divers while reducing the risk of their injury. This study details the development, fabrication, and simulation of a cost-effective subaquatic manipulator, denoted as MURA, which can be seamlessly incorporated into submersible vehicles. The Kalypso unmanned underwater vehicle (UUV) is utilized in this study. MURA exhibits a high degree of modularity, enabling seamless alteration of the end-effector tool. Additionally, its low-cost nature renders it a viable option for integration with any underwater vehicle. Three end-effectors were subjected to testing, one designed for the purpose of disposing fish morts, another intended for removing litters from net cages in fisheries, and a third for repairing net tears. This study outlines the MURA design, including the arm’s fabrication and constituent components. In addition, the modeling of the manipulator is presented accompanied by a water flow simulation of the three manipulators. Ultimately, the experimental findings are analyzed and evaluated. These include the field experiments performed at Kefalonia fisheries, along with the duration to complete each task. For instance, the capture of fish morts typically necessitates approximately 30 s, encompassing the entire process from initial targeting to actual capture. In a similar vein, the procedure of mending tears in a net necessitates an approximate duration of 70 s on average, encompassing the stages of initial identification and subsequent detachment. The suggested design exhibits adaptability and durability while upholding affordability when utilized in aquaculture.

Design and analysis of a flexible hydrophone based on PVDF with aluminum film substrate and PDMS encapsulation

Unmanned underwater vehicles (UUVs) need hydrophones for underwater acoustic sensing. However, previous hydrophones were rigid in shape and unable to realize conformal installation. In this paper, a flexible hydrophone with great installation adaptability was presented. Polyvinylidene fluoride (PVDF) piezoelectric film was applied as the transducing material. A flexible aluminum film was selected as the substrate. These two films were sandwiched by polydimethylsiloxane (PDMS) layers. Finite element simulation revealed that the sensitivity and bandwidth of the hydrophone were enhanced by using the aluminum film substrate. The sensing performance improvement was ascribed to the suppressed in-plane stress of the PVDF film. Prototypes were fabricated and tested. The fabricated hydrophone exhibited a sensitivity of −211 dB (ref. 1 V/μPa) at 500 Hz. It owned a 20–500 Hz working frequency range and an omnidirectional pattern. Besides, the sensitivity difference caused by shape changes was within 2.3 dB. Furthermore, the hydrophone showed good waterproofing ability.

Robust adaptive three-dimensional trajectory tracking control for unmanned underwater vehicles with disturbances and uncertain dynamics

Aiming at the three-dimensional (3-D) trajectory tracking problem of UUV with uncertain dynamics in an unknown and bounded external disturbance environment, a new dual closed-loop controller based on nonlinear model predictive control (NMPC) and adaptive integral sliding mode control (AISMC) is designed. The outer-loop NMPC controller designed based on position and attitude tracking errors generates a virtual desired velocity command and transmits it to the inner-loop controller. The inner-loop adaptive integral sliding mode controller based on speed tracking error design combines adaptive radial basis (RBF) neural network controller and disturbance observer to approximate the uncertainty of system model parameters and environmental disturbances, and then generates the actual control input to the UUV system. Different from other cascaded control systems, the designed controller combines the advantages of display processing constraints of MPC and robustness of SMC, which can improve the robustness of the control system, it effectively considers the actual constraints of the system input and state, and avoids the saturation of the actuator. In addition, the stability of the closed-loop system with and without disturbances is proved respectively based on the Lyapunov method. Finally, simulation results verify the effectiveness of the proposed controller: Under the perturbation of 10% model parameters, the UUV can accurately track the desired trajectory.

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas.

New Technologies to Assess and Map an Urban Riparian Area in Drama, Greece, and Determine Opportunity Sites for Litter Traps

Riparian areas offer many ecosystem services, especially in urban settings. Their conservation can be complex because of the many urban anthropogenic pressures they face. Adopting new technological approaches can provide insights on the most cost-effective and sustainable management for riparian areas. In this study, different new technological approaches were implemented to assess and map environmental variables and find the optimal location of nature-based solutions (e.g., litter traps). The study area was Agia Varvara Park in Drama, Greece, a unique natural urban riparian area. The approaches utilized were categorized as aerial, terrestrial, and surface/underwater. Specifically, these approaches included unmanned aerial vehicles that incorporated high-resolution regular and thermal cameras to capture the surface environmental conditions and unmanned underwater vehicles to capture the underwater environmental conditions. The produced orthomosaics and digital surface models enabled us to estimate the boundaries of the water surface in Agia Varvara Park. A GPS tracker was also used to record the potential movement route of litter. Finally, a sonar device was utilized to estimate the water depth of potential cross-sections of Agia Varvara’s stream where the litter trap could be installed. The above datasets were used to develop spatial datasets and accompanying maps that were utilized to find the optimal opportunity sites for the litter trap. A litter trap is a floating device that gathers and maintains litter, vegetation, and other debris. Two specific locations were proposed based on water presence, water depth, channel’s width, limited vegetation for accessibility, wildlife existence, litter’s water route, and stopping location time. Such traps enable the collection of anthropogenic litter. In one location, a litter trap has been installed and is being tested. Overall, the above approaches could be used to suggest other nature-based solutions and/or their optimal location, thus enhancing the sustainable management of urban riparian areas.

An end-to-end neural network for UUV autonomous collision avoidance

This paper proposes a 3D autonomous collision avoidance method based on convolutional gated recurrent units to improve the autonomy of Unmanned Underwater Vehicle (UUV). The state equations of the UUV autonomous collision avoidance system are constructed by studying its mechanism and integrating dynamic/static obstacle recognition, dynamic obstacle motion prediction, collision risk assessment, and collision avoidance. Then a multi-input single-output neural network architecture that integrates static feature extraction, dynamic time sequence modeling, and feature integration is proposed based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to describe the state space. CNNs extract features from sonar observation data to improve the accuracy of obstacle recognition. GRUs are combined with CNNs to capture the correlation of long-distance features and extract dynamic features. The spatial and temporal invariance of the neural network architecture enhances the fault tolerance of the UUV collision avoidance system for inputs and adaptability to observation noise and environments. Finally, simulation results show that this method is adaptable to sonar observation noise and unknown environments to solve the problem of forward-looking sonar-based UUV collision avoidance in unknown complex ocean environments.