Operated Vehicle Research Articles

A Study on Kinematics, Dynamics, and Fuzzy Logic Controller Design for Remotely Operated Vehicles

A Study on Kinematics, Dynamics, and Fuzzy Logic Controller Design for Remotely Operated Vehicles

Reconstruction of the Maërl habitat to better understand its ecological integrity

Maërl habitats are composed of coralline red algae species that can live freely rolling on the seabed and forming nodules, the so-called rhodoliths, or incrusted forming coralligenous habitats. Maërl habitats are generally distributed in the Mediterranean at a depth of between 30 m and 70 m and are considered one of the most emblematic Mediterranean seabeds. In the present study, the complex structure of maërl habitats was investigated to i) characterise the relief features and classify the different sediments, ii) to estimate the abundance of the coralline red algae (both rhodoliths and encrusting ones) and iii) to analyse the biodiversity of the species inhabiting the habitat. Data were obtained from an approximately 11 km-long transect, using non-intrusive sampling methods, integrating information from video images collected using the Remotely Operated Vehicle LIROPUS (IEO_CSIC), and multibeam bathymetry and backscatter data. Video images were used to reconstruct (using GIS) the habitat structure and characteristics. Throughout the transect, a strong relationship between habitat characteristics and the effect of trawling activity and the geomorphology of the studied area was observed. The closed area to fishing activity showed a high abundance of rhodoliths in well-structured megaripples reliefs. Contrarily, the areas affected by fishing showed an important destructuring of the relief with a low density of rhodoliths. Last, the muddy bottoms showed areas with no characteristic features and no rhodoliths. All this information has allowed to reconstruct the maërl habitat in the Blanes continental shelf (NW Mediterranean) and analyse the fragmentation of the assemblages seen in the video to assess its good environmental status (GES), and finally to identify the level of ecological integrity of this vulnerable habitat.

Energy harvesting potential assessment and systematic design for energy-regenerative shock absorbers on railway freight wagons

A large amount of vibration energy is dissipated in the secondary suspension systems of railway freight wagons, which can be harvested as renewable power supplies to serve more smart devices for onboard applications. This paper explores the vibration energy harvesting potential of freight wagons and deals with the systematic design issues of energy-regenerative shock absorbers (ERSAs). By considering the ERSA force interaction and realistic track irregularity, a vehicle-track coupled model is established to predict a more accurate vibration response. The parameter sensibility analysis reveals that the operation speed, vehicle load, and track irregularity are the most critical factors that can significantly affect the power generation performance. In addition, vibration energy harvesting potential assessment is conducted on American, German, and Chinese track spectrums and several field-measured freight lines, indicating an average power potential ranging from 33 to 960 W per absorber with a full-loaded freight wagon running at 90 km/h. Finally, a systematic design approach for ERSAs is proposed based on the prior feasibility assessment, a hybrid Grey Wolf Optimization and Particle Swarm Optimization (GWO-PSO) algorithm, and the vehicle-ERSA coupled model. The digital twin of an ERSA has been established and validated by a series of experimental tests. Taking the average power as the objective and setting the suspension vibration velocity, maximum generator rotation velocity, and maximum ERSA force as constraints, the optimized ERSA exhibits an output power of 63 W and 20.22% shock absorption on the secondary suspension. Meanwhile, the GWO-PSO has demonstrated an enhanced exploration ability than the conventional GWO in dealing with the constrained optimization problem of the ERSA design.

Research on collision avoidance simulation for autonomous underwater vehicles using ant colony optimization algorithm

Autonomous Underwater Vehicles (AUVs) are increasingly crucial in exploiting and exploring marine resources. Route building and obstacle avoidance are the core technologies that help this device perform aquatic processes. Collision avoidance methods use acoustic sensors and sonar systems to help detect targets, but these devices have difficulty seeing at short distances. This article proposes a method to simulate the autonomous process of a Remotely Operated Vehicle (ROV) or AUV in a virtual environment based on several path planning and obstacle avoidance algorithms. At the same time, the author used sonar data and optimization algorithms in the route construction process to address the limitations of AUVs and ROVs and the influence of the marine environment. Finally, through Unity 3D software, simulate the underwater operation of the AUV and check the algorithm results.

State of health estimation and prediction of electric vehicle power battery based on operational vehicle data

State of health estimation and prediction of electric vehicle power battery based on operational vehicle data

DEVELOPMENT OF UNDERWATER ROV (REMOTELY OPERATED VEHICLE) SYSTEMS AND MECHANISM

Remotely Operated Vehicle or abbreviated as ROV is an underwater explorer robot where the robot is controlled remotely by an operator using a remote control system. Water ecosystems certainly have various kinds of hazards that cause harm to divers, such as wild animals, waters contaminated with toxic waste, limited oxygen, and many other risks. Based on these problems, research was carried out, namely the Design and Development of Underwater ROV (Remotely Operated Vehicle) Mechanisms and Systems where the tool built is an underwater robot that uses 8 DC motors to be able to perform 12 types of movements under water. The final result obtained by the ROV is that it is able to perform all of these movements except for the right somersault and left somersault. The speed and direction of rotation of the motor is controlled by a device, namely the BTS7960 motor driver. This robot aims to measure pressure and temperature and capture images underwater to a depth of 4m. Pressure and temperature measurements were made using a BMP180 pressure sensor and a DS18B20 temperature sensor where the measurement results will be displayed on a 16x2 LCD. Based on the test results, the pressure sensor has an error rate of 3.6% and a temperature sensor of 0.34%. Images and videos are captured by a device, namely a CCTV camera where the device is connected to a laptop device as a monitor. The test produced results in the form of images and videos that were quite clear.

Methane-flow system within the Nyegga pockmark field, offshore mid-Norway

We investigated fluid seepage within the Nyegga pockmark field (600–900 m water depths) off mid-Norway from Remotely Operated Vehicle dives at the so-called CNE sites (CNE01 to CNE17). The seafloor morphology of some of these features corresponds to pockmarks and adjacent ridges, with the latter being the focus of present seepage activity. These structures are underlain by chimneys above a gas-charged zone with, in some cases, a substantial body of hydrate-invaded sediment (down to 1.3 s in two-way travel time at CNE03). Present-day methane-rich fluid seepage through the seabed is indicated by chemosynthetic fauna, in particular Siboglinidae polychaetes (Oligobrachia haakonmobiensis webbi and Sclerolinum contortum), microbial mats and associated Rissoidae gastropod (Alvania sp.) grazers, and confirmed by measured in situ bottom-water methane anomalies, up to 2,130 nL/L. No free-gas bubble emissions were observed or acoustically identified. The presence of authigenic carbonates reveals past seepage with very low δ13C values (down to −58‰) indicating that the major source of carbon was methane carried by the venting fluids. The ages of major periods of methane venting are provided by vesicomyid bivalve shells (Isorropodon nyeggaensis) present in two sedimentary layers, 14,930 and 15,500 14C yr BP (ca. 17,238 and 17,952 cal yr BP), respectively, corresponding to the time of Melt Water Pulse IA. The seafloor morphology and pattern of seepage -chemosynthetic fauna and microbial mat distribution and dissolved methane concentration-are remarkably heterogeneous. Pore-water chemistry profiles in a gravity core taken only 40 m from major seepage sites indicate no seepage and anaerobic methane oxidation at a sub-bottom depth of about 2 m. Present-day seepage from the studied pockmark-chimney fluid-flow system charged with gas hydrate is dominated by the advection of methane solution in pore water. Some of this methane could result from the dissolution of hydrate in the chimney, most of which would have formed during an earlier period (post-LGM times) of history of the chimney, when it was venting free gas. However, the presence of free gas beneath this chimney is probably why the water entering the chimney is already saturated with methane and the process of hydrate formation in the chimney continues today.

Large gravitational collapse structure on a rocky coast (Kvarner, NE Adriatic Sea)

The studied rock collapse structure is located on the Liburnian coast (Rijeka Bay, channel zone of the NE Adriatic). The relief of the southern part of this coast, with a length of 6.5 km, is a large escarpment with very steep to vertical slopes reaching heights of 100 m above sea level, as a result of tectonic movements along the Kvarner fault zone. These events probably led to a sudden relaxation of the highly fractured rock mass. The progressive expansion occurred at locations where previously favourably oriented faults and fissures had formed a polygonal rock collapse resembling a rock-slide which is the focus of this study. Another aim of this study is to reconstruct and explain the complex morphological evolution of the studied landslide, from the pre-failure deformations, through the failure itself, to post-failure displacements, as well as possible future instabilities. Recent techniques to survey the instability, location and to analyse the evolution of the rupture surface and its dimensions were combined (Unmanned Aerial Vehicle, Side Scan Sonar and Remotely Operated Vehicles). The estimated total volume of displaced rock mass is 950,000 m3. The lower part of the instability phenomenon was submerged during the Holocene sea level rise. Since then, a large part of the displaced rock mass has been in a stable position, with sporadic rock falls. However, given unfavourable orientation and discontinuity characteristics, as well as unfavourable environmental influences, possible instabilities might also be expected in the future.

Analisis Pengadaan Kendaraan Dinas Operasional Melalui Sistem Pembelian

In carrying out the main duties of the agency, the government requires a procurement system to meet operational needs. One of the procurements carried out is the procurement of operational service vehicles. This research is descriptive qualitative by using data collection techniques through observation, interviews, studying existing documents and conducting case studies in the field to obtain conclusions about the procurement system used and provide suggestions for future program development.The results showed that the two-wheeled service vehicle procurement system through the purchasing system was effective and provided benefits. While the procurement of four-wheeled official vehicles through the purchasing system has not been effective and efficient, therefore an alternative rental system is needed to provide many benefits.

A cascaded model predictive control based trajectory tracking controller for an autonomous and remotely operated vehicle with LOS guidance

ABSTRACT Autonomous and Remotely Operated Vehicle (ARV) are widely used for underwater detection and typically require ARVs to track predefined trajectories. Manual operation is not very accurate and stable, and intelligent control methods are popularly devised. This paper firstly introduces the line of sight (LOS) method for desired heading guidance. Later, a cascaded model predictive control (MPC) is proposed. In the cascaded MPC, a quantum-behaved particle swarm optimisation algorithm based on a non-linear decreasing contraction-expansion coefficient (QPSO-CE-nonlinear) is applied to optimise the controller’s output. Several validation and comparative simulations are carried out. The results show that the controller has advantages in accuracy, convergence, and stability. The LOS is applicable to holonomic ARV and accelerates convergence. The QPSO-CE-nonlinear algorithm can improve the rate of convergence when solving optimisation problems. In addition, compared with MPC-SMC (sliding mode control), the cascaded MPC is more robust.

Coordinated Hierarchical Co-Optimization of Speed Planning and Energy Management for Electric Vehicles Driving in Stochastic Environment

Active co-optimization of future speed profiles together with powertrain control is the optimal solution to further exploiting the energy benefit of electric vehicles (EVs) in real-world operation. However, with uncertainties in driving conditions and concerns about driving safety, speed planning results are cautious and with frequent speed variations, which deteriorates the energy economy of EVs in turn. To comprehensively optimize the energy economy and driving safety of EVs in a stochastic driving environment, this paper develops a chance constraint model predictive control (CC-MPC) for co-optimizing the speed planning and powertrain control, which forms an advanced energy management method. To handle the instantaneous disturbance, a coordinated hierarchical method (CHM) is engineered for solving the CC-MPC. As suggested by simulation, the driving safety (measured by success rate) can be increased to 81% with the CC-MPC, which realizes a 62% improvement compared with situations without CC-MPC. Moreover, the proposed CC-MPC significantly mitigates the conflict between driving safety and the energy economy, and the worst deterioration of the energy economy is only 9.3%. Sacrificing merely 2.1% sub-optimality, CHM removes 86% computation loads, and the median of CPU time is merely 0.58s at each computation step (control interval 1s), which makes the CC-MPC promising for online implementation.

Monitoring of Critical Undersea Infrastructures: The Nord Stream and Other Recent Case Studies

The explosions on September 26th, 2022, which damaged the gas pipelines of Nord Stream 1 and Nord Stream 2, have highlighted the need and urgency of improving the re- silience of Critical Undersea Infrastructures (CUIs). Comprising gas pipelines and power and communication cables, these con- nect countries worldwide and are critical for the global economy and stability. An attack targeting multiple of such infrastruc- tures simultaneously could potentially cause significant damage and greatly affect various aspects of daily life. Due to the in- creasing number and continuous deployment of CUIs, existing underwater surveillance solutions, such as Autonomous Under- water Vehicles (AUVs) or Remotely Operated Vehicles (ROVs), are not adequate enough to ensure thorough monitoring.We show that the combination of information from both un- derwater and above-water surveillance sensors enables achieving Seabed-to-Space Situational Awareness (S3A), mainly thanks to Artificial Intelligence (AI) and Information Fusion (IF) method- ologies. These are designed to process immense volumes of infor- mation, fused from a variety of sources and generated from mon- itoring a very large number of assets on a daily basis. The learned knowledge can be used to anticipate future behaviors, identify threats, and determine critical situations concerning CUIs.To illustrate the capabilities and importance of S3A, we con- sider three events that occurred in the second half of 2022: the aforementioned Nord Stream explosions, the cutoff of the under- water communication cable SHEFA-2 connecting the Shetland Islands and the UK mainland, and the suspicious activity of a large vessel in the Adriatic Sea. Specifically, we provide analy- ses of the available data, from Automatic Identification System (AIS) and satellite data, integrated with possible contextual in- formation, e.g., bathymetry, Patterns Of Life (POLs), weather conditions, and human intelligence (HUMINT).

Impacts of COVID-19 on Travel Behaviors: The Case of Izmir

With the emergence of the COVID-19 pandemic, passenger preferences for transportation modes and transportation management strategies have rapidly changed in the last few years. In this study, field research was conducted to reveal the behaviors and perceptions of passengers before and during the COVID-19 pandemic in Izmir city of Turkey. A total of 1437 participants were asked to evaluate the pre and during pandemic periods with their frequency of use for different types of transportation modes in Izmir. When the results of the study were evaluated holistically, it was revealed that the participants used environmentally friendly transportation modes such as bicycles and e-bikes more than before the pandemic and that they wanted to use them after the pandemic. Despite this, it is seen that the use of taxis and private vehicles has increased during the pandemic, especially for long-distance trips, and the use of public transport and light rail systems (LRS) in general has decreased. Nearly half of respondents (49.7%) said bus drivers did not use adequate protective equipment during the pandemic. In addition, 41.1% of the participants stated that the HES code application was not applied adequately in public transportation. In the study, it was also revealed that the practice of reducing the number of operational public transport vehicles during the pandemic was found to be negative by the participants.

Can we use normal saline stored under stress conditions? A simulated prehospital emergency medical setting

BackgroundData on stability and suitability to use normal saline stored under stress conditions in ambulances is lacking. ObjectiveWe aimed to study the impact of exposure to extreme temperature variations on normal saline stability and compatibility with its packaging. MethodsNormal saline in 96 polyolefin bags were exposed to continuous temperature of 22, 50, and 70 °C or to a cyclic temperature of 70 °C per 8 h and 22 °C per 16 h. The bags were sampled at 12, 24, 48 and 72 h and at 1, 2, 3, and 4 weeks in the short- and long-term experiments, respectively. Solution inside the bags was evaluated for any evidence of crystallization, discoloration, turbidity, or pH changes. A sample of normal saline was withdrawn from each bag to analyze sodium and chloride levels. ResultsPrecipitation, discoloration, or turbidity were not observed in the solution inside normal saline bags. The average pH was 5.59 at 22 °C, 5.73 at 50 °C, 5.86 at 70 °C and 5.79 at cyclic exposure. In the short- and long-term experiments, sodium and chloride concentrations were within 100.2–111.27% and 99.04–110.95%, respectively. Leaching of the plastic components in the polyolefin bag into the normal saline solution was not detected. ConclusionsSodium and chloride levels of normal saline were stable and compatible with polyolefin bags stored in simulated continuous and cyclic extreme temperatures for around one month. The effect of storage in the cabinet of operational ambulance vehicles during different seasons in arid countries is yet to be evaluated in real-world conditions, to further confirm our results.

DFFA-Net: A Differential Convolutional Neural Network for Underwater Optical Image Dehazing

This paper proposes DFFA-Net, a novel differential convolutional neural network designed for underwater optical image dehazing. DFFA-Net is obtained by deeply analyzing the factors that affect the quality of underwater images and combining the underwater light propagation characteristics. DFFA-Net introduces a channel differential module that captures the mutual information between the green and blue channels with respect to the red channel. Additionally, a loss function sensitive to RGB color channels is introduced. Experimental results demonstrate that DFFA-Net achieves state-of-the-art performance in terms of quantitative metrics for single-image dehazing within convolutional neural network-based dehazing models. On the widely-used underwater Underwater Image Enhancement Benchmark (UIEB) image dehazing dataset, DFFA-Net achieves a peak signal-to-noise ratio (PSNR) of 24.2631 and a structural similarity index (SSIM) score of 0.9153. Further, we have deployed DFFA-Net on a self-developed Remotely Operated Vehicle (ROV). In a swimming pool environment, DFFA-Net can process hazy images in real time, providing better visual feedback to the operator. The source code has been open sourced.

Assessing the effect of onshore and offshore Wave Energy Converters on seafloor integrity combining image-based and acoustic methods

The European Atlantic Ocean offers great opportunities for the development of projects for renewable energy extraction, and the Marine Renewable Energy sector is developing different technologies for energy converters, including Wave Energy Converters. Besides, the European Commission is adopting measures and politics to increase the installed capacity of ocean energy. However, there are still uncertainties on the potential environmental effects of wave energy converters, which led regulators and stakeholders to perceive their operation as a risky activity. To overcome the non-technological barriers that could hinder the development of marine renewable energies, and improve the knowledge on the impacts on the seafloor integrity (among others), SafeWAVE project set an Environmental Research Demonstration Strategy based on the collection, processing, modelling, analysis and sharing of environmental data collected in Aguçadoura (Portugal), Armintza (Spain) and Le Croisic (France) representing different types of technology, locations and project scales. Video recordings were carried out, using Remotely Operated Vehicles, to identify the environmental impacts of the moorings on the seafloor morphology. That information was completed by side scan sonar campaigns using an Autonomous Underwater Vehicle. The video recordings were useful also to assess the attraction effect of the moorings to epibenthic invertebrates and fishes, whereas the side scan sonar provided quantitative information on the area affected by physical alteration (<1% of the total area occupied by the devices at Armintza and Le Croisic).

Computational fluid dynamic study on design and modification of underwater remotely operated vehicle

Remotely Operated Vehicle (ROV) has been widely used in numerous underwater exploration applications such as exploration of complex deep sea environment, pipeline detection, installation, maintenance, and repair. Thus, it is worthy to design, optimize, and performance evaluation of ROV systems for underwater exploration purposes. Computational Fluid Dynamic (CFD) is a computational approach that is very helpful and widely used to examine the flow characteristics of ROV systems. In this research work, CFD analyses of ROV bodies with different thruster blades were carried out in order to generate thrust forces by the thruster blades. For this purpose, ANSYS FLUENT was used to examine the flow characteristics over the ROV body. The analyses were performed at different flow velocities, such as 1 m/s, 1.5 m/s, 2 m/s, 2.5 m/s, 3 m/s and 3.5 m/s. The outcomes were in the form of thrust force, drag force, pressure distribution, and velocity distribution. The result shows that four-blade thruster generates maximum thrust force as compare to other ROV thruster models. The main reason behind this higher thrust force is due to the larger motion of the fluid. Similarly, drag force was also investigated and observed that the drag force is greater for thruster model 3, having four blades due to the large frontal area of the ROV body and thruster blades. The results obtained in this study are very helpful for engineers and researchers to design and optimize ROV models using numerical methods.

Design and Fabrication of a Low-Cost Multi-Purpose Underwater Remotely Operated Vehicle

Remotely Operated Underwater Vehicles (ROVs) are emerging potential technology for ocean research, environmental and geochemical studies, mine hunting, surveillance, and commercial usage. Due to their affordability, simplicity in handling and deployment, appropriateness for deep-sea diving, increased mobility, and ability to operate in harsh settings, underwater vehicles—both autonomous and remotely controlled—have grown in popularity. The goal is to develop and test a remotely operated underwater vehicle (ROV) that is lightweight, affordable, and capable of conducting surveys in shallow waters. The design, production, and experimental findings of such an ROV are the focus of this paper. The mechanical design, thruster characteristics, electrical systems, and software architecture of the graphical user interface (GUI) for the proposed vehicle are all thoroughly explained. In contrast to autonomous underwater vehicles, the planned ROV is totally controlled from the base station using a live camera feed and other sensors (AUV). In contrast to the majority of AUVs, it provides real-time results from its various sensors and takes human commands via two-way communication. Comprehensively addressed, the results and answers are provided. Constraints on the mechanical design’s weight and portability, electronics’ power requirements, and the length of the umbilical cord are all addressed. It is also explored how to strengthen the paradigm and make it a for-profit enterprise with more improvements.

Environmental Mapping of Underwater Structures Based on Remotely Operated Vehicles with Sonar System

Recently, underwater robotics has rapidly developed, and is often used in open-water exploration and underwater operations in known environments. However, there are still several problems in exploring the interiors of complex underwater environments, which are essential for scientific exploration and industrial applications, such as caves and shipwrecks. This study aims to complete the exploration of the environment of structures under water bodies. A real-time manipulative small underwater robot was designed and developed. The robot’s autonomous depth control and linear motion-assisted control are also realized by real-time sensor data processing, which provides stability and operability to move in small areas and complex environments. The sonar system is used to construct a submap for small-area scanning. Finally, by combining the odometer algorithm and contour extraction, the submaps are stitched together to construct a complete map of the internal underwater environment.

The Polarized Light Field Enables Underwater Unmanned Vehicle Bionic Autonomous Navigation and Automatic Control

In response to the critical need for autonomous navigation capabilities of underwater vehicles independent of satellites, this paper studies a novel navigation and control method based on underwater polarization patterns. We propose an underwater course angle measurement algorithm and develop underwater polarization detection equipment. By establishing the automatic control model of an ROV (Remote Operated Vehicle) with polarization information, we develop a strapdown navigation method combining polarization and inertial information. We verify the feasibility of angle measurement based on polarization in the water tank. The measurement accuracy of polarization azimuth is less than 0.69°. Next, we conduct ROV navigation at different water depths in a real underwater environment. At a depth of 5 m, the MSE (Mean Square Error) and SD (Standard Deviation) of angle error are 16.57° and 4.07°, respectively. Underwater navigation accuracy of traveling 100 m is better than 5 m within a depth of 5 m. Key technologies such as underwater polarization detection, multi-source information fusion, and the ROV automatic control model with polarization have been broken through. This method can effectively improve ROV underwater work efficiency and accuracy.