Research on Hydrodynamics Model Test for Deepsea Open-Framed Remotely Operated Vehicle

This paper presents the features of newly designed hydrodynamics test for the scaled model of 4500 m deepsea open-framed remotely operated vehicle （ROV）, which is being researched and developed by Shanghai Jiao Tong University. Accurate hydrodynamics coefficients measurement and spatial modeling of ROV are significant for the maneuverability and control algorithm. The scaled model of ROV was constructed by 1：1.6. Hydrodynamics coefficients were measured through VPMM and LAHPMM towing test. And dynamics model was derived as a set of equations, describing nonlinear and coupled 5-DOF spatial motions. Rotation control motion was simulated to verify spatial model proposed. Research and application of hydrodynamics coefficients are expected to enable ROV to overcome uncertainty and disturbances of deepsea environment, and accomplish some more challengeable and practical missions.

OBLIQUE TOWING TEST AND MANEUVER SIMULATION AT LOW SPEED AND LARGE DRIFT ANGLE FOR DEEP SEA OPEN-FRAMED REMOTELY OPERATED VEHICLE

This article presents the newly designed oblique towing test in the horizontal plane for the scaled model of 4 500 m deep sea open-framed Remotely Operated Vehicle （ROV）,which is being researched and developed by Shanghai Jiao Tong University.Accurate hydrodynamics coefficients measurement is significant for the maneuverability and control system design.The scaled model of ROV was constructed by 1：1.6.Hydrodynamics tests of large drift angle were conducted through Large Amplitude Horizontal Planar Motion Mechanism （LAHPMM） under low speed.Multiple regression method is adopted to process the test data and obtain the related hydrodynamic coefficients.Simulations were designed for the horizontal plane motion of large drift angle to verify the coefficients calculated.And the results show that the data can satisfy with the design requirements of the ROV developed.

Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance

Underwater imaging is being used increasingly by marine biologists as a means to assess the abundance of marine resources and their biodiversity. Previously, we developed the first automatic approach for estimating the abundance of Norway lobsters and counting their burrows in video sequences captured using a monochrome camera mounted on trawling gear. In this paper, an alternative framework is proposed and tested using deep-water video sequences acquired via a remotely operated vehicle. The proposed framework consists of four modules:(1) preprocessing,(2) object detection and classification,(3) object-tracking, and(4) quantification. Encouraging results were obtained from available test videos for the automatic video-based abundance estimation in comparison with manual counts by human experts(ground truth). For the available test set, the proposed system achieved 100%precision and recall for lobster counting, and around 83% precision and recall for burrow detection.

An approach to operational risk modeling and estimation of safety levels for deep water work class remotely operated vehicle-A case study with reference to ROSUB 6000

This paper presents a quantitative approach to operational risk modeling and estimation of safety integrity levels,required for the deep water electric work class remotely operated vehicle with reference to ROSUB6000 developed by the National Institute of Ocean Technology,India.ROSUB6000 is used for carrying out bathymetric surveys,gas hydrate surveys,poly-metallic nodule exploration,salvage operations,and meeting emergency response situations.The system is expected to be in operation for a period of 300 h per year,and has to be extremely safe and reliable.Methods and models for the quantitative assessment of operational safety and estimation of safety integrity levels for ROV are seldom available in the deep water intervention industry.The safety instrumented functions implemented in the ROV should be able to meet the SIL requirements of specific mission.This study indicates that the required safety factors are implemented into the design of the state-of-the-art ROV ROSUB 6000,considering IEC 61508/61511 recommendations on Health,Safety and Environment and it is found that the system is able to meet the required SIL for seven identified functions.This paper gives the design and safety engineers in the ROV industry,an overview of the numerical operational risk assessment methods and safety-centered ROV engineering.

Development of a Robot for In-service Radiography Inspection of Subsea Flexible Risers

The extreme operational environmental conditions and aging conditions of subsea structures pose a risk to their structural integrity and is critical to their safety.Nondestructive testing is essential to identify defects developing within the structure,allowing repair in a timely manner to mitigate against failures that cause damage to the environment and pose a hazard to human operators.However,to be cost effective,inspections must be carried out without taking the risers out of service.This poses significant safety risks if undertaken manually.This paper presents the development of an automated inspection system for flexible risers that are used to connect wellheads on the seafloor to the offshore production and storage facility.Due to the complex structure of risers,radiography is considered as the best technique to inspect multiple layers of the risers.However,radiography inspection,in turn,requires a robotic system for in-situ inspection with higher payload capacity,precise movement of source and detector which is able to withstand an extreme operational environment.The deployment of a radiography inspection system hasbeen achieved bydeveloping acustomized subsearobotic system called RiserSure that can provide precise scanning motion of a gamma ray source and digital detector moving in alignment.The prototype has been tested on a flexible riser during shallow water sea trials with the system placed around a riser by a remotely operated vehicle.The results from the trials show that the internal inner and outer tensile armor layer and defects in the riser can be successfully imaged in real operational conditions.

A Hull-Inspect ROV Control System Architecture

The control architectures of ＂SY-2＂ Remote Operated Vehicle （ROV） are introduced. Both hardware architecture and software architecture are discussed. PC/104 embedded computer is used to control equipment for collecting sensor data and sending control commands. PC/104 embedded computer is integrated with A/D, D/A, 8 serial ports card and power supply unit. The surface computer is a X86PC. They transfer data through a fiber line. For software, real-time OS VxWorks is embedded in PC/104. A/D, D/A and serial ports operation are based on VxWorks OS, which increase the real-time quality of control system. Surface computer is the center of motion control and data processing. It is communicated with underwater PC/104 by socket. The whole system has been tested both on land and in tank.

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater structures.Research to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale cracks.In this paper,a large-scale underwater crack examination method is proposed based on image stitching and segmentation.In addition,a purpose of this paper is to design a new convolution method to segment underwater images.An improved As-Projective-As-Possible(APAP)algorithm was designed to extract and stitch keyframes from videos.The graph convolutional neural network(GCN)was used to segment the stitched image.The GCN’s m-IOU is 24.02%higher than Fully convolutional networks(FCN),proving that GCN has great potential of application in image segmentation and underwater image processing.The result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

多用途远程操作的海面运载器

Development of man-packable,versatile marine surface vehicle with ability to rescue a drowning victim and also capable of carrying mission specific sensor is explored.Design thinking methodology is implemented by using existing equipment/platform with the addition of external attachment to make it a functional product.Iterative prototyping process with extensive testing to achieve user-centric solution.Individual prototypes and their possible sub-configurations with their integration and characteristics are studied and compared with numerical model,inferences obtained are utilised to improve for the next iteration.A novel hinge-clamp assembly enables this marine surface vehicle to operate in the event of an overturn,this phenomenon is further studied with the aid of a mathematical model(Pendulum in a fluid).This research project aims to demonstrate a multi-role unmanned surface vehicle.

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

The inspection of water conveyance tunnels plays an important role in water diversion projects.Siltation is an essential factor threatening the safety of water conveyance tunnels.Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects.The remotely operated vehicle(ROV)can detect such siltation.However,it needs to improve its intelligent recognition of image data it obtains.This paper introduces the idea of ensemble deep learning.Based on the VGG16 network,a compact convolutional neural network(CNN)is designed as a primary learner,called Silt-net,which is used to identify the siltation images.At the same time,the fully-connected network is applied as the meta-learner,and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results.Finally,several evaluation metrics are used to measure the performance of the proposed method.The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%,which is far better than the accuracy of other classifiers.Furthermore,the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.