Indian national student AUV competition: A success story

With a large coastline of approximately 7600 km surrounded by two major ocean basins on both sides of peninsular India as Bay of Bengal and Arabian Sea, drained by major river basins from Himalayas, there are multidimensional requirement of underwater technologies to cater the country's demand. With increased thrust on underwater technology in India during the past 15 years, it is imperative to put forth India's development in the frontier area of underwater technologies. Under the Ministry of Earth Sciences, Government of India, National Institute of Ocean Technology (NIOT) is leading the frontier areas of underwater technologies. The Institutes like Central Mechanical Engineering Research Institute and Indian Institute of Technologies are also contributing in a minor way. Under International Seabed Authority (ISA) regulations, Government of India registered as a contractor on 17th August 1987. India was allocated 150,000 sq. km area in Central Indian Ocean Basin for exploration of manganese nodules. After detailed exploration, 50% of this area has been relinquished to the ISA. While other Institutes in India are responsible for metal extraction, NIOT is responsible for developing technology for mining of manganese nodules from the deep seabed. To harness the non-renewable resources ranging from placer deposits at water depth of 100 m, gas hydrates at 1000 m, hydrothermal sulphides at 3000 m to polymetallic nodules at 5400 m water depth, various technologies were developed and proven in the field. To cater to the disaster management, range of observation systems, drifters and seafloor based observations are being developed and data collection and dissemination is in place. This paper deals with the achievements in the development of underwater vehicles and systems during the past 15 years in India in the civilian front. The major technologies developed, like the Deep sea crawler (512 m) for mining of manganese nodules, In-situ soil tester (5462 m) for measurement of in-situ soil property on the sea bed, Work Class Remotely Operated Vehicle (5289 m) for general purpose, including assistance in nodule mining, Autonomous Underwater Vehicle (200 m) for shallow water and operation in polar regions, being developed, and drifter buoys for collection of ocean data are explained in detail. The challenges involved in design, development, testing and issues faced during the sea trials of the various systems and lessons learnt are explained in this paper.

AUV (autonomous underwater vehicle) design is requiring the coordination of many different disciplines, which involved in assemblity, mission and performance. Conceptual design is the least defined stage of the AUV design process. An approach for multidisciplinary optimization, multi-objective optimization study of AUV's principal parameters in conceptual design is proposed in present analysis. First, a MDO (Multi-Disciplinary Optimization) flow is constructed for AUV design circle. The total system can be divided into one system control level and 6 sub-systems. Then, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to search the design space for optimal, feasible designs by considering three measures of performance (MOPs): cost, effectiveness, and risk. The result shows that, NSGA-II is effective in multi-objective optimization of an AUV conceptual design.

Design and construction of an autonomous underwater vehicle

It is said generally that the history of autonomous underwater vehicles (AUVs) started with SPURV which was developed by APL of the University of Washington at the request of US Navy in 1957. This was an untethered underwater vehicle with a total length of 3.1 m and a displacement of 430 kg driven by a mono-axis motor and a silver-zinc battery. It was able to run with a maximum depth of 3600 m, a speed of 2–2.5 kt and a maximum cruising endurance of 5.5 h. For control, 12 bit preset command was used to change the steps of azimuth and depth, and hardwired logic circuit was used in the controller. The azimuth was controlled by the offset from the initial azimuth at launch. The roll is secured by the static stability of the body. The temperature and conductivity sensors were installed on the nose section, and the data was recorded on a tape recorder. This vehicle was used for wide-area ocean observation by US Navy.1 A modern AUV, for example, the REMUS 600 developed by Woods Hole Oceanographic Institution having the same size scale as SPURV, has an inertial navigation system of ring laser gyro fully integrated with a Doppler velocity log (DVL) and acoustic positioning system. Autopilot software enables independent control of fins providing yaw, pitch and roll control, altitude, depth, and track-line following. Furthermore, it can be equipped with optional forward fins available to maintain a straight heading in a cross current. This alignment and stability is essential for optimizing the performance of a synthetic aperture sonar. Various kind of sensors like synthetic aperture sonar (SAS), side scan sonar (SSS), and multibeam echo sounder (MBES) can be equipped as the payload sensor. This can run for up to 24 h with lithium-ion rechargeable batteries and it is also possible to avoid obstacles by using a front sonar.2 In a half century, due to the progress of digital technologies, sensor technology, and battery technologies, AUVs have made significant progress and they have been used widely for oceanography, industries, and defense. One of the most representative examples of AUV's strengths in wide-area subsea search was for Air France Flight 447 which crashed off the coast of Brazil in 2009. Multiple REMUS 6000, which is equipped with SSS and cameras, were used in this search activity, and after about 2 months of operation, they finally discovered the wreckage of an aircraft lying about 4000 m deep seabed and contributed to the recovery of the flight and voice recorders.3 From the perspective of industry, the AUV industry has moved from prototype development for research and development to mass production of systems now. However, the number of AUV manufactures is still limited, for example, “Husin” of Kongsberg in Norway,4 “Remus series” of Hydroid in United States (now a subsidiary of Huntington Ingalls Industries), and “Bluefin series” of Bluefin Robotics in United States (a subsidiary of General Dynamics Mission Systems).5 Mitsubishi Heavy Industries, Ltd. (MHI) had developed R&D AUV such as Urashima6 for JAMSTEC, and currently is manufacturing OZZ -5 AUV production model for the Ministry of Defense in Japan.7 This article mainly describes the expected future operation of AUVs to motivate AUV research and development in the academic world. This section describes the typical operation of an AUV, which is a prerequisite for a concrete view of the control system. As its name implies, AUV is an autonomous underwater vehicle that does not have a tether cable for power supply and communication. After it is launched from the surface vessel, it basically navigates autonomously according to a predetermined route plan because its underwater communication capacity is very limited. The first AUV, SPURV, traveled through the “empty” ocean to measure sea water temperature and conductivity. Therefore, it did not have sensors to detect the surrounding terrain. On the other hand, the main mission of the modern AUV is to survey the seafloor structure, or objects such as unexploded ordnance (UXO) using an underwater acoustic sonar. Since the AUV navigates at a relatively low altitude, it navigates autonomously while recognizing the seafloor and obstacles using DVL. This can detect the vertical altitude, and the forward looking sonar, which can detect obstacles and the seafloor ahead. In addition, in order to cover the search area with on-board sensors, a typical mission pattern is to cruise at a certain depth or altitude in a certain pattern (typically a lawnmower pattern), and maintain a constant speed so that the sensor detection range covers the search area. This mission pattern is planned and preset as a route plan before launch. In order to acquire data of the sonar, especially in the case of the synthetic aperture sonar, it is necessary to suppress the attitude fluctuations and keep straight for a crossing tidal current using a forward rudder as well. The AUV basically runs along a preset route plan unless there is an obstacle or emergency. After the crushing of the mission pattern is completed, AUV returns to the planned position and is recovered by the surface vessel. The acquired data is transferred to the computer on the vessel, and maintenance work such as battery charging is conducted, and then, the operation is repeated. Since the amount of the underwater acoustic sonar data is enormous and the transmission by underwater communication is limited, the data is evaluated by the human for the first time after the data analysis on the vessel. The next route plan may then be corrected depending on the data acquisition results. It should be noted that launch and recovery, especially recovery, is troublesome and dangerous work at sea due to the lack of a tether cable for the AUV, and the limitations under rough sea conditions. This leads to mission standby on the vessel and a resulting cost. After the AUV undersea survey, ROV may be used for detailed observation and underwater work depending on the purpose of operation. This is because the resolution of acoustic sonar is not as fine as the visual method, although it is possible to detect an “object like the target.” So the ROV needs to identify and determine “the target” using a visual or optical method, which has finer resolution than acoustic. Conventional AUV operation such as seabed surveys, or unknown object is almost the same procedure as described. Unlike such conventional operations, innovative technological developments for more efficient operations have been carried out for some applications. One such development is AUV's underwater docking technology, which has already been tried in various research studies. This allows the AUV to dock with an underwater docking station, charging electrical power to the AUV and transmitting data from the AUV, continuously to operate without launch and recovery. This has the possibility of providing innovative efficient operation of the AUV. To realize underwater docking, there are technical problems of a proximity sensor, a docking device, and guidance and attitude control. In some docking systems, mono-axial propulsion AUV, which is difficult to cruse at low speed, is caught by pushing it into a docking cage. However, an AUV has protrusions such as Fin, GPS/Wi-Fi antennas, and acoustic communication devices and also has a rubber surface for acoustic sensors. So this approach is risky for the AUV itself. Therefore, in consideration of the future expansion of AUV operations to be described later, it would be more feasible to adopt an approach in which an AUV capable of low speed or hovering is guided to precise docking positions and attitude, stabilized, and safely captured. Furthermore, the docking technology also can be applied to automatic launch and recovery operations. In order to improve the search efficiency, it is easy to come up with multiple AUV operations. In fact, several AUV's were used in the aforementioned Air France 447 search. Although it is only necessary to allocate each search area, it is necessary to introduce the viewpoint of automatic optimizing route planning. This is in order to eliminate duplication and omission of search areas, to consider work procedures such as launch and recovery, to adapt and easily change the plan according to the search situation, and to improve the overall efficiency. Furthermore, it will be possible that the operation of multiple vehicles will reach the level of Swarm operation by the ultra-small AUV. Airborne drones have been demonstrated to control 1000 groups, however they are still nothing more than technical interesting in terms of practical operations. Innovative practical ideas are required. In addition to multiple AUV operation, it is necessary to automatically optimize by combining heterogeneous unmanned vehicles in consideration of the entire practical operation, such as by cooperating with an autonomous surface vehicle (ASV) and the ROV. Even in the conventional operation pattern, there are technical problems to improve the search efficiency of AUV, and some of them are introduced below. At present, the AUV only controls the operation of its mission sensors such as SSS according to the pre-determined route plan. However, it is also conceivable that the search will be more effective by changing the route plan autonomously in real time according to the state of data acquisition by the mission sensor. For example, it is conceivable that, as a result of the automatic detection of a target like object in real time by a wide-area search sensor such as SSS, the wide-area search can be temporarily suspended, and then circling around the target area to obtain multi-angle data. It is also conceivable closing to the target to obtain the detailed data by using a fine resolution sensor such as underwater camera. This type of advanced operation eliminates the need for a second time operation, such as the “reacquisition” operation by ROVs, after the operation of the AUV, and will greatly improve the overall operation. Of course, some operators want to avoid the AUV having unintended behavior, so it is necessary to consider that the operator makes the final decision by using underwater communications. Such advanced autonomous behavior requires not only real-time automatic recognition technology of the sensor technology, but also the AUV itself is capable of low-speed cruising or hovering for closing approach to the target object. Both local route planning for the closing approach and route replanning to restart wide area search are necessary in real time and optimally. Despite the innovative autonomy in the previous section, there is a need to cruise at a lower altitude at a lower speed that is difficult for control. This is in order to obtain more detailed acoustic data or to survey with a short-range high-resolution sensor such as an underwater camera or laser. The lower the altitude, the more meticulous the route planning, and the more precise tracking and attitude control, considering sensor conditions, are required for complex terrains. : Vehicle velocity (velocity and angular velocity for each three axes). : Mass and added mass property terms. : Coriolis and centripetal terms. : Hydrodynamic damping force terms. : Hydrostatic force terms related to the Euler angle : Generalized actuator force and moment terms. There have been problems with the AUV control, such as disturbances caused by tidal currents, uncertainty of the model characteristics (e.g., fluid dynamics coefficients) and sensor data, nonlinearity of the actuators. The control engineers have designed and implemented an acceptable control system against such problems by conventional PID methods, linearizing the equation of motion and separating the control plane: vertical (pitch and depth) and horizontal (yaw and roll). However, PID control will be no longer applicable, when the AUV mission requires various maneuvers over a wide range of speeds including hovering, and side thrusters have been added to the AUV. AUV control engineers are facing the problems, such as, onboard reroute-planning, precise nonlinear maneuvering control in a wide range of velocities, while satisfying optimality and constraints such as energy limitations, attitude limitations for acquiring sensor data and actuator limitations in addition to the conventional problems of disturbances and uncertainties in the model. As for the development process, model-based development has already become main stream of control, and it is not necessary to discuss the merits of model-based developments here. In addition to this, it is expected that the development environment, simulation environment, and implementation environment will be seamlessly linked for an efficient development environment. In addition, considering the verification in three dimensions, such as in the complicated terrain tracking, it is difficult to evaluate only by the conventional time history graph. Visualization tools such as three-dimensional animation are required. Furthermore, considering the direct sharing of development methods and results, and the indirect sharing through education and human resource exchange, standardization of development, evaluation, and implementation environments is strongly required. De facto standards have already emerged. In this way, the efficiency of the development process on desk work has been improved, however when it comes to field testing, it remains unchanged. Running a system for the first time in water without tether cables and relying solely on acoustic communication, is still a major risk. After the initial test is completed and the vehicle starts to run, it does not follow the simulation because the AUV's body characteristics, especially the hydrodynamic characteristics, have the uncertainties mentioned. After correcting the control gain by trial-and-error, it should finally run as intended. Previously, the evaluation items were less due to its simple mission. However, in future, when AUV missions will be complex and evaluation items will become larger, we will not able to do such trial-and-error testing by human engineers. It leads to a huge number of sea trials and costs can be enormous. To avoid these high cost situations, adaptive methods are desirable. Adaptive control methods have been researched in the past, and recently, AI or re-enforcement learning is a rapidly growing area. We expect it will contribute in minimizing the development cost and operational cost. In the field of aerial robotics, the spread of low cost GPS, MEMS gyros, and microcomputers, as well as the development of software and network technologies, has led to the remarkable success of quadcopters. Drone has becomes the synonym of quadcopter although it means all kinds of unmanned robot. Drone is rapidly expanding their application from industrial applications such as surveying, agriculture, and entertainment to individual hobbies. Land robots are also a huge industry in terms of autonomous driving. On the other hand, underwater robots require special know-how such as pressure resistant structures and watertight structures in an underwater environment, and they are difficult to be recover when water leaks and sinks due to a small mistake. In addition, much of the equipment on board is expensive, such as underwater acoustic devices. For this reason, only a few manufacturers and research institutes with many years of experience in underwater vehicles can produce them, and their use is still limited to some marine industries and to defense fields. However, when considering the maturity of the technology, it is important to expand the scope of the industry, and for that purpose, it is important to reduce the AUV cost itself and its operating costs. From the development process of AUV to the improvement of operational efficiency by the innovation and upgrading of the operation, and so forth, the role of the control system is huge. We conclude with the hope that research and development and education on AUV control systems will continue to develop. I would like to thank to Chief Editor, Prof. M. J. Grimble to give me a chance to contribute this article, and Prof. I. Yamamoto, to advise and review the contents. And I am grateful to my colleagues on discussion about AUV control for the future.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

Autonomous Underwater Vehicles (AUVs) have been established as a viable tool for Oceanographic Sciences. Being untethered and independent, AUVs fill the gap in Ocean Exploration left by the existing manned submersible and remotely operated vehicles (ROV) technology. AUVs are attractive as cheaper and efficient alternatives to the older technologies and are breaking new ground in many applications. Designing an autonomous vehicle to work in the harsh environment of the deep ocean comes with its set of challenges. This paper discusses how the current engineering technologies can be adapted to the design of AUVs. Recently, as the AUV technology has matured, we see AUVs being used in a variety of applications ranging from sub-surface sensing to sea-floor mapping. The design of the AUV, with its tight constraints, is very sensitive to the target application. Keeping this in mind, the goal of this thesis is to understand how some of the major issues affect the design of the AUV. This paper also addresses the mechanical and materials issues, power system design, computer architecture, navigation and communication systems, sensor considerations and long term docking aspects that affect AUV design. With time, as the engineering sciences progress, the AUV design will have to change in order to optimize its performance. Thus, the fundamental issues discussed in this paper can assist in meeting the challenge of maintaining AUV design on par with modern technology.

Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks. Reinforcement learning (RL) and deep reinforcement learning have been introduced into the AUV design and research to improve its autonomy. However, these methods are still difficult to apply directly to the actual AUV system because of the sparse rewards and low learning efficiency. In this paper, we proposed a deep interactive reinforcement learning method for path following of AUV by combining the advantages of deep reinforcement learning and interactive RL. In addition, since the human trainer cannot provide human rewards for AUV when it is running in the ocean and AUV needs to adapt to a changing environment, we further propose a deep reinforcement learning method that learns from both human rewards and environmental rewards at the same time. We test our methods in two path following tasks—straight line and sinusoids curve following of AUV by simulating in the Gazebo platform. Our experimental results show that with our proposed deep interactive RL method, AUV can converge faster than a DQN learner from only environmental reward. Moreover, AUV learning with our deep RL from both human and environmental rewards can also achieve a similar or even better performance than that with deep interactive RL and can adapt to the actual environment by further learning from environmental rewards.

As more and more attention is paid to the ocean in today's world, autonomous underwater vehicle (AUV) is widely used. At present, a variety of AUVs has been put into commercial use. For AUV, its underwater resistance is very important to its performance of AUV, and the geometry of AUV is the key factor affecting the underwater resistance. Considering the difficulty and cost of production, most commercial AUVs adopt a torpedo rotary design. In this paper, for the commercial small AUV, we make an optimal design of its head by a parabola. Compared with the popular commercial AUV product, REMUS100, the underwater resistance of AUV design is analyzed by ANSYS Fluent calculation. We also analyze the head stress of AUV by ANSYS Mechanical. The head optimization design of AUV based on the parabola is obtained, which provides a reference for the design of small commercial AUVs in the future.

Estuaries are a challenging environment to use acoustically navigated Autonomous Underwater Vehicles (AUVs) due to highly variable currents, relatively shallow and variable bathymetry, large buoyancy changes, suspended sediments, marine biota, and bubble plumes. The benefit to using AUVs is their ability to perform repeat automated surveys and targeted sampling of features using either remote control or on-board redirects. Our REMUS 100 AUVs are equipped with up/down looking ADCPs, CTDs, and optical backscatter sensors. Our AUVs use Long Base Line (LBL) underwater navigation, with up to four transponders, and were recently equipped to carry broadband hydrophones. We will share our experience operating these AUVs in several estuaries to characterize variability in optical and acoustical backscatter associated with estuarine features of interest (fronts, river plume, and the salt-wedge). We will discuss how these features negatively impact AUV sampling via, for example, degraded underwater communications and bottom tracking. We will also present some examples of concurrent sampling by AUVs and an advanced sonar (static and mobile) demonstrating the potential use of AUVs in estuarine research for 4D visualization of estuarine features of interest. [This work was supported by Office of Naval Research.]

This paper presents the design and development of a new Autonomous Underwater Vehicle (AUV). SHAD, which stands for Small Hovering AUV with Differential actuation, is a torpedo shaped vehicle that was conceptually designed to navigate in challenging volumes. It brings to the scene of submarine robotics a different model and new design of AUV. The small size, the light weight and the high maneuverability of this AUV were among the most important features that can make the SHAD an option to applications where other models have difficulties. This paper details the design and the development of SHAD and presents experimental results from sensors and actuators testing as well as vehicle navigation. © 2016 IEEE.

<div class="htmlview paragraph">Hydrodynamic parameters play a major role in the dynamics and control of Autonomous Underwater Vehicles (AUV). The performance of an AUV is dependent on the parameter variations and a proper understanding of these parametric influences is essential for the design, modelling and control of high performance AUVs. In this paper, a six sigma framework for the sensitivity analysis of a flatfish type AUV is presented. Robust design techniques such as Taguchi’s design method and statistical analysis tools such as Pareto-ANOVA, and ANOVA are used to identify the hydrodynamic parameters influencing the dynamic performance of an AUV. In the initial study, it is found that when the vehicle commanded in forward direction, it is in bow down configuration which is unacceptable for AUV motion. This is because of the vehicle buoyancy and shape of the vehicle. So the sensitivity analysis of pitch angle variation is studied by using robust design techniques. Six prominent hydrodynamic coefficients are considered for the analysis. The results show that there are three critical hydrodynamic parameters such as buoyancy, added mass, and hydrodynamic moment in heave motion that influence the performance of a flat fish type AUV. These findings are significant in the design modifications as well as controller design of AUV.</div>

Propulsion and maneuvering of autonomous underwater vehicles require a combination of effective and efficient operation at both high and low speeds. The collective and cyclic pitch propeller (CCPP) is a novel system designed to provide the required operational flexibility through control of the propeller's blade pitch. Collective pitch control governs the forward generated thrust, whereas cyclic pitch control governs the generated maneuvering force(s)/side-force(s). In this article, a numerical analysis into the CCPP's hydrodynamic performance at bollard pull is set-up, reducing the complex three-dimensional flow problem to a two-dimensional problem. Through a force break-down model, the CCPP's hydrodynamic performance is related and matched to the operation of a pitching hydrofoil. Analysis of the two-dimensional numerical results can thereby provide insights into the performance of the three-dimensional CCPP. First, the performance of the pitching hydrofoils is investigated as such, relating the generated lift, drag, and moment to the occurrence of dynamic stall. Next, the methodology's applicability and limitations are discussed by comparing the numerical results with recent experiment CCPP work to allow the model to be used for a numerical evaluation of the CCPP's performance. Under the evaluated conditions, testing a range of collective and cyclic pitch angles under bollard pull, the side-force generation by the CCPP is shown to be highly dependent on the generated drag force at higher collective pitch angles. At low pitch angles, the side-force generation is controlled by the lift produced over the pitching blades, and efficient but not highly effective. As the collective pitch is increased, the generated drag affects both the effectiveness of the side-force and the side-force efficiency, defined by the large resulting side-force orientation. At larger collective pitch angles, the lift forces are overtaken by the drag generation, resulting in effective but inefficient side-force generation. Autonomous underwater vehicles (AUVs) have become a widely used and researched tool for underwater exploration and reconnaissance (Alam et al. 2014). AUVs distinguish themselves from other unmanned underwater vehicles in their ability to complete a pre-determined mission autonomously over large distances and long time periods, i.e., without the need for regular human interaction. The diversity in industry applications for AUVs has resulted in a wide range of AUV shapes and designs (Button et al. 2009). Applications include different areas such as underwater pipe-line inspection in the oil and gas industry, sample collection for marine biology research, and military surveillance missions (Chyba 2009). One key requirement of any AUV design, as a result of their specific mission profile and inherent functionality, is the combination of efficient long-endurance travelling capabilities with effective maneuverability at low speeds (Wernli 2000). Traditional maneuvering systems using control surfaces lose their efficiency at low speeds and lowspeed maneuvering aids such as side-or podded-thrusters reduce the long-endurance travelling efficiency. A novel propulsion and maneuvering system, aimed at providing both efficient long-endurance propulsion and effective maneuvering at all speeds, is the collective and cyclic pitch propeller (CCPP).

Liu, G.; Chen, G.; Jiao, J., and Jiang, R., 2015. Dynamics modeling and control simulation of an autonomous underwater vehicle. A dynamics model of an open-shelf Autonomous underwater vehicle (AUV) is described in this paper. The virtual prototype technology and the control simulation software are used to build the virtual prototype model of AUV, and AUV dynamic location control arithmetic is simulated based on analyzing motion and hydrodynamic mathematical model of the virtual prototype. The simulation results indicate that the virtual prototype system has the function of simulation demo and performance validation, and can provide one kind of new method for AUV graphic simulation, and has very important practical meaning on AUV design and control research.

Autonomous underwater vehicles (AUVs) navigating in complex sea conditions usually require a strong control system to keep the fastness and stability. The nonlinear trajectory tracking control system of a new AUV in complex sea conditions was presented. According to the theory of submarines, the six-DOF kinematic and dynamic models were decomposed into two mutually non-coupled vertical and horizontal plane subsystems. Then, different sliding mode control algorithms were used to study the trajectory tracking control. Because the yaw angle and yaw angle rate rather than the displacement of the new AUV can be measured directly on the horizontal plane, the sliding mode control algorithm combining cross track error method and line of sight method was used to fulfill its high-precision trajectory tracking control in the complex sea conditions. As the vertical displacement of the new AUV can be measured, in order to achieve the tracking of time-varying depth signal, a stable sliding mode controller was designed based on the single-input multi-state system, which took into account the characteristic of the hydroplane and the amplitude and rate constraints of the hydroplane angle. Moreover, the application of dynamic boundary layer can improve the robustness and control accuracy of the system. The computational results show that the designed sliding mode control systems of the horizontal and vertical planes can ensure the trajectory tracking performance and accuracy of the new AUV in complex sea conditions. The impacts of currents and waves on the sliding mode controller of the new AUV were analyzed qualitatively and quantitatively by comparing the trajectory tracking performance of the new AUV in different sea conditions, which provides an effective theoretical guidance and technical support for the control system design of the new AUV in real complex environment.

Recently, the importance of design process with unknown parameter increased. On the other hand, the design of Autonomous Underwater Vehicles (AUVs) is a difficult challenge since it requires the consideration of various aspects such as mission range, controllability, energy source, and carrying capacity. A design process for novel type of AUV constructed using an origami-based structure that includes active material actuators and solar panels is proposed in this paper. To increase the efficiency in the three-dimensional shape modeling of the AUV, the shape of the outer surface is parameterized by a finite set of variables using shape functions. Here, the AUV should operate underwater via electrical power with the batteries being charged periodically using solar panels. The ability of the AUV to transport cargo such as instrumentation is also addressed. The design parameters include the total height and width of the AUV. As these dimensions of the AUV might vary in a non-preferential manner based on particular mission goals, these dimensions are considered as design parameters in a multi-objective optimization setting. The Predictive Parameterized Pareto Genetic Algorithm (P3GA) is selected as the optimization method to determine a Pareto frontier of design options with desired characteristics for a variety of missions for the AUV. The evaluation of each AUV design entails quantitative assessment of the origami fold pattern determined using a method developed by the authors and Computational Fluid Dynamics (CFD) analysis. The development of a design process that addresses the design optimization of the AUV considering its hydrodynamic performance and origami aspects is the main topic of this paper.