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Imagine trying to navigate through your environment while looking straight ahead through a narrow tube. These are essentially the conditions the operator of an ROV in teleoperation mode may have to contend with. We hypothesized that controlling ground-based ROVs would be easier if an operator developed an explicit overview of the space in which the ROV was maneuvering. Accordingly, we conducted a study in which we had naive undergraduate participants explore a maze-like virtual desert environment while drawing a map of the area. After completing a 30-min mapping task, participants re-entered the maze to search for and retrieve a target object. The virtual robots and landscape, which had minimal landmarks and a maze of navigable paths (see Fig. 1), were created using CeeBot (see Chadwick, Gillan, Simon, & Pazuchanics, 2004, for a discussion of the CeeBot tool). Maps drawn by participants were rated independently by three raters (graduate psychology students) for the usefulness of the map for navigating the area on a scale from 1 (not at all useful) to 7 (extremely useful). Cronbach's alpha was computed as a consistency estimate of inter-rater reliability at 0.96.

A novel proportional integral derivative-extended state disturbance observer-based control (PID-ESDOBC) algorithm is proposed to solve the nonlinear hydrodynamics, parameters perturbation and external disturbance in yaw control of remote operated vehicles (ROVs). The effectiveness of PID-ESDOBC is verified through the experiments and the results indicate that the proposed method can effectively track the desired attitude and attenuate the external disturbance.

This study fully investigates the hydrodynamic model of ROVs and proposes a control-oriented hydrodynamic state space model of ROVs in yaw direction. Based on this, this study designs the PID-ESDOBC controller, whose stability is also analyzed through Kharitonov theorem and Mikhailov criterion. The conventional proportional-integral-derivative (PID) and active disturbance rejection control (ADRC) are compared with our method in our experiment.

In this paper, the authors address the nonlinear hydrodynamics, parameters perturbation and external disturbance problems of ROVs with multi-vector propulsion by using PID-ESDOBC control scheme. The advantage is that the nonlinearities and external disturbance can be estimated accurately and attenuate promptly without requiring the precise model of ROVs. Compared to PID and ADRC, both in overshoot and settling time, the improvement is 2X on average compared to conventional PID and ADRC in the pool experiment.

The delays occurred in the control process can be solved in the future work.

The attitude control is a kernel problem for ROVs. A precise kinematic and dynamic model for ROVs and an advanced control system are the key factors to obtain the better maneuverability in attitude control. The PID-ESDOBC method proposed in this paper can effectively attenuate nonlinearities and external disturbance, which leads to a quick response and good tracking performance to baseline controller.

The PID-ESDOBC algorithm proposed in this paper can be ensure the precise and fast maneuverability in attitude control of ROVs or other underwater equipment operating in the complex underwater environment. In this way, the robot can better perform undersea work and tasks.

The dynamics of the ROV and the nominal control model are investigated. A novel control scheme PID-ESDOBC is proposed to achieve rapidly yaw attitude tracking and effectively reject the external disturbance. The robustness of the controller is also analyzed which provides parameters tuning guidelines. The effectiveness of the proposed controller is experimental verified with a comparison by conventional PID, ADRC.

A novel fault-tolerant control (FTC) method is proposed to assure the stability of the remote-operated vehicle (ROV) by considering the thruster failure-induced model perturbations. The stability of the ROV with failures is guaranteed and optimized with the determined model perturbation set. The effectiveness of the double-boundary interval fault-tolerant control (DBIFTC) is verified through the experiments and proves that the stability is well maintained, which demonstrates a decent performance.

This paper studies a control problem for a multi-vector propulsion ROV by using the DBIFTC method in the presence of thruster failure and external disturbances. The ROV kinematics and dynamical models with multi-vector-arranged thruster failure are investigated and formulated for control system design.

In this paper, the authors address the FTC problem of ROV with multi-vector thrusters and propose a DBIFTC scheme. The advantage is that as the kinematic system model of ROV is preanalyzed and identified, the DBIFTC becomes more effective. The mathematical stability of the system under the proposed control scheme can be guaranteed.

The ROV model used in this paper is based on the system identification of experimental data. Although this model has real experimental value and physical significance, the accuracy can be further improved.

Cable-controlled underwater ROVs are widely used in military missions and scientific research because of their flexibility, sufficient load capacity and real-time information transmission characteristics. The DBIFTC method proposed in this paper can effectively reduce the problem of underwater vehicle under propeller failure or external disturbance and save unnecessary cost.

The DBIFTC method proposed in this paper can ensure the attitude stability of ROV or other underwater equipment operating in the event of propeller failure or external disturbance. In this way, the robot can better perform undersea work and tasks.

The kinematics and failure mechanisms of the ROV with multi-vector propulsion system are investigated and established. An optimized DBIFTC scheme is investigated to stabilize ROV yaw attitude under the thruster failure condition. The feasibility and effectiveness of the DBIFTC is experimentally validated.

The purpose of this study is to develop a new positioning method for remotely operated vehicle (ROV) in the nuclear power plant. The ROV of the nuclear power plant is developed to inspect the reactor cavity pools, the component pools and spent-fuel storage pools. To enhance the operational safety, the ability of localizing the ROV is indispensable.

Therefore, the positioning method is proposed based on the MEMS inertial measurement unit and mechanical scanning sonar in this paper. Firstly, the ROV model and on board sensors are introduced in detail. Then the sensor-based Kalman filter is deduced for attitude estimation. After that, the positioning method is proposed that divided into static positioning and dynamic positioning. The improved iterative closest point-Kalman filter is deduced to estimate the global position by the whole circle scanning sonar data in static, and the relative positioning method is proposed by the small scale scanning sonar data in dynamic.

The performance of the proposed method is verified by comparing with the visual positioning system. Finally, the effectiveness of the proposed method is proved by the experiment in the reactor simulation pool of the Daya Bay Nuclear Power Plant.

The research content of this manuscript is aimed at the specific application needs of nuclear power plants and has high theoretical significance and application value.

Proposing a fuzzy multi-criteria decision making (MCDM) algorithm that is able to incorporate the heterogeneousness effect of DM group into the decision process, in order to determine the best remotely operated vehicle (ROV) design alternative to manufacture and developing a practical decision aid tool based on this algorithm. The paper aims to discuss these issues.

An algorithm utilizes fuzzy AHP Buckley’s approach for modeling heterogeneousness of the DM group, fuzzy AHP Chang’s extent analysis to calculate the priority values of criteria and Chen’s fuzzy TOPSIS for ranking the alternatives and finally group working technique for initiation issues is developed. MATLAB is used to implement the algorithm and generate a decision aid tool. Real life application and sensitivity analysis is performed by the help of generated tool. Literature and background explanations are also provided.

A MCDM algorithm that incorporates the heterogeneousness effect of the DM group into the decision process is introduced. Sensitivity analysis suggested the independence of the final result from DM group and criteria set. A practical decision aid tool is generated for ROV manufacturing companies.

A computerized MCDM aid tool that incorporates heterogeneousness of the DM group into the decision process is generated. Tool let ROV manufacturing companies to evaluate ROV design alternatives with respect to qualitative and quantitative criteria and determine proper choice.

Determination of the proper ROV design alternative to manufacture gap within the literature filled with an algorithm that provides more reliable results due to its incorporation the heterogeneousness of the DM group into the decision process characteristic. A practical decision aid tool is generated.

The ROV ground control simulator (Fig. 1) used in this multi-sensory research consists of two workstations: pilot and SO. At the left workstation, the pilot controls ROV flight (via stick-and-throttle inputs as well as invoking auto-holds), manages subsystems, and handles external communications. From the right workstation, the SO is responsible for locating and identifying points of interest on the ground by controlling cameras mounted on the ROV. Each station has an upper and a head-level 17″ color CRT display, as well as two 10″ head-down color displays. The upper CRT of both stations displays a ‘God's Eye’ area map (fixed, north up) with overlaid symbology identifying current ROV location, flight waypoints, and current sensor footprint. The head-level CRT (i.e., “camera display”) displays simulated video imagery from cameras mounted on the ROV. Head-up display (HUD) symbology is overlaid on the pilot's camera display and sensor specific data are overlaid on the SO's camera display. The head-down displays present subsystem and communication information as well as command menus. The simulation is hosted on four dual-Pentium PCs. The control sticks are from Measurement Systems Inc. and the throttle assemblies were manufactured in-house.

Firms can choose from an array of approaches for reducing the detrimental financial effects caused by unfavorable fluctuations in commodity prices. The purpose of this paper is to provide guidance for effectively estimating the financial effects of mitigating commodity price risk volatility (CPV) in supply chain management decisions.

This paper adopts two prominent and complementary methodologies, namely, total cost of ownership (TCO and real options valuation (ROV), to illustrate how commodity price risk mitigation strategies can be analyzed with respect to their effect on costs and performance. The paper provides insights through a case study to demonstrate the application of these methods together and establish the benefits and challenges associated with their implementation.

The paper illustrates advantages and disadvantages of TCO and ROV and how these approaches can be adopted together to contribute to effective purchasing decisions. Supply chain flexibility is a key capability but requires investments. Holistically measuring the financial effects of flexibility investments is imperative for gaining executive management support in mitigating commodity price volatility.

This study can provide supply chain professionals with useful guidance for measuring the costs and benefits related to developing strategies for mitigating commodity price volatility. TCO provides a focus on the costs associated with the commodity purchasing process, and ROV enables the aggregation of all the costs and benefits associated with the use of the strategy and synthesizes them into the net value estimate.

The paper provides a comparison of different but complementary approaches, specifically TCO and ROV, for analyzing the effectiveness of CPV risk mitigation decisions. In addition, these two methods allow supply chain professionals to evaluate and control the financial effects of CPV risk, particularly the impact of mitigation on firm’s cash flows.

To understand the importance of coordination and collaboration for ROV teams, let us examine some of the typical tasks that ROV operators might be required to perform (Cooke & Shope, 2004; Gugerty, DeBoom, Walker, & Burns, 1999). To do so, we will use the members of a U.S. Air Force Predator crew as an example. The team consists of three members: an Air Vehicle Operator (AVO) who pilots the aircraft, a Payload Operator (PLO) who operates the surveillance equipment, and a Data Exploitation, Mission Planning, and Communications Operator (DEMPC) who is responsible for mission planning. In the course of a mission, the AVO is responsible for the take off and landing of the aircraft. Because they fly the aircraft from a remote location, AVOs are generally required to use visual input from a camera mounted on the nose of the aircraft to guide their flight. Once in the air, the PLO can operate cameras and sensors mounted on the belly of the plane to gather information. The DEMPC, who is in contact with the upper echelons of the organization, provides the AVO with the desired heading and the PLO with target coordinates.

This paper presents the results of a heading estimation method for a remotely operated vehicle (ROV). The output rate of commercially available underwater compasses is typically in the order of a few Hz. Heading frequencies of at least 1 KHz are desirable for navigation and control purposes.

The estimation was performed by fusioning the signals of three inertial sensors: the ROV’s own underwater compass (which operates roughly at 10 Hz or less), the ROV’s embedded gyro and an additional angular rate sensor that provides readings from 1 to 3 KHz. The output signal of the additional angular rate sensor is not part of the proposed Kalman filter. Nonetheless a five-point Newton-Cotes closed integration of such signal is fed into the Kalman filter implementation that performs the required heading estimation at 1 KHz or more.

The proposed Kalman filter implementation is a suitable approach to estimate heading position even though the original compass signal rate is significantly slower than the signal required for both assisted and autonomous control.

The estimated heading yield good results in both simulation and experimental environments.

The method was embedded in a dedicated 16-bit DSP that handles both the acquisition of the three signals and the heading estimation, hence resulting in a very low-cost solution. The embedded solution was tested in the developed submarine and the obtained high-rate heading parameter is now used by the control system of the ROV.

As they are currently conducted, missions by single ROVs consist of several sub-tasks. After a vehicle has been launched, a human operator or a small team is responsible for controlling the flight, navigation, status monitoring, flight and mission alteration, problem diagnosis, communication and coordination with other operators, and often data analysis and interpretation. These tasks are similar in terms of their locus of control (e.g., keyboard and mouse input, joystick, trackball, visual display).