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Author: Charmane Venda Caldwell Publisher: ISBN: Category : Languages : en Pages : 97
Book Description
ABSTRACT: In recent years there has been a demand from the commercial, research and military industries to complete tedious and hazardous underwater tasks. This has lead to the use of unmanned vehicles, in particular autonomous underwater vehicles (AUVs). To operate inthis environment the vehicle must display kinematically and dynamically feasible trajectories. Kinematic feasibility is important to allow for the limited turn radius of an AUV, while dynamic feasibility can take into consideration limited acceleration and braking capabilities due to actuator limitations and vehicle inertia. Model Predictive Control (MPC) is a method that has the ability to systematically handle multi-input multi-output (MIMO) control problems subject to constraints. It finds the control input by optimizing a cost function that incorporates a model of the system to predict future outputs subject to the constraints. This makes MPC a candidate method for AUV trajectory generation. However, traditional MPC has difficulties in computing control inputs in real time for processes with fast dynamics. This research applies a novel MPC approach, called Sampling-Based Model Predictive Control (SBMPC), to generate kinematically or dynamically feasible system trajectories for AUVs. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC, namely large computation times and local minimum problems. SBMPC is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A?) in place of standard nonlinear programming. SBMPC can avoid local minimum, has only two parameters to tune, and has small computational times that allows it to be used online fast systems. A kinematic model, decoupled dynamic model and full dynamic model are incorporated in SBMPC to generate a kinematic and dynamic feasible 3D path. Simulation results demonstrate the efficacy of SBMPC in guiding an autonomous underwater vehicle from a start position to a goal position in regions populated with various types of obstacles.
Author: Charmane Venda Caldwell Publisher: ISBN: Category : Languages : en Pages : 97
Book Description
ABSTRACT: In recent years there has been a demand from the commercial, research and military industries to complete tedious and hazardous underwater tasks. This has lead to the use of unmanned vehicles, in particular autonomous underwater vehicles (AUVs). To operate inthis environment the vehicle must display kinematically and dynamically feasible trajectories. Kinematic feasibility is important to allow for the limited turn radius of an AUV, while dynamic feasibility can take into consideration limited acceleration and braking capabilities due to actuator limitations and vehicle inertia. Model Predictive Control (MPC) is a method that has the ability to systematically handle multi-input multi-output (MIMO) control problems subject to constraints. It finds the control input by optimizing a cost function that incorporates a model of the system to predict future outputs subject to the constraints. This makes MPC a candidate method for AUV trajectory generation. However, traditional MPC has difficulties in computing control inputs in real time for processes with fast dynamics. This research applies a novel MPC approach, called Sampling-Based Model Predictive Control (SBMPC), to generate kinematically or dynamically feasible system trajectories for AUVs. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC, namely large computation times and local minimum problems. SBMPC is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A?) in place of standard nonlinear programming. SBMPC can avoid local minimum, has only two parameters to tune, and has small computational times that allows it to be used online fast systems. A kinematic model, decoupled dynamic model and full dynamic model are incorporated in SBMPC to generate a kinematic and dynamic feasible 3D path. Simulation results demonstrate the efficacy of SBMPC in guiding an autonomous underwater vehicle from a start position to a goal position in regions populated with various types of obstacles.
Author: Yang Shi Publisher: Springer Nature ISBN: 3031193547 Category : Technology & Engineering Languages : en Pages : 210
Book Description
This book provides a comprehensive overview of marine control system design related to underwater robotics applications. In particular, it presents novel optimization-based model predictive control strategies to solve control problems appearing in autonomous underwater vehicle applications. These novel approaches bring unique features, such as constraint handling, prioritization between multiple design objectives, optimal control performance, and robustness against disturbances and uncertainties, into the control system design. They therefore form a more general framework to design marine control systems and can be widely applied. Advanced Model Predictive Control for Autonomous Marine Vehicles balances theoretical rigor – providing thorough analysis and developing provably-correct design conditions – and application perspectives – addressing practical system constraints and implementation issues. Starting with a fixed-point positioning problem for a single vehicle and progressing to the trajectory-tracking and path-following problem of the vehicle, and then to the coordination control of a large-scale multi-robot team, this book addresses the motion control problems, increasing their level of challenge step-by-step. At each step, related subproblems such as path planning, thrust allocation, collision avoidance, and time constraints for real-time implementation are also discussed with solutions. In each chapter of this book, compact and illustrative examples are provided to demonstrate the design and implementation procedures. As a result, this book is useful for both theoretical study and practical engineering design, and the tools provided in the book are readily applicable for real-world implementation.
Author: Chao Shen Publisher: ISBN: Category : Languages : en Pages :
Book Description
The increasing reliance on oceans, rivers and waterways in a spectrum of human activities have demonstrated the large demand for advanced marine technologies that facilitate multifarious in-water services and tasks. The autonomous underwater vehicle (AUV) is a representative marine technology which has been contributing continuously to many ocean-related fields. An elaborate control system is essential to AUVs. However, AUVs present difficult control system design problems due to their nonlinear dynamics, the unpredictable environment and the poor knowledge about the hydrodynamic coupling of the vehicle degrees of freedom. When designing the motion controller, the practical constraints on the AUV system such as limited perceiving, computing and actuating capabilities should also be respected. The model predictive control (MPC) is an advanced control technology that leverages optimization to calculate the control command. Thanks to the optimization nature, MPC can conveniently handle the complex nonlinearity in system dynamics as well as the state and control constraints. MPC takes the receding horizon control paradigm which gains satisfactory robustness against model uncertainties and external disturbances. Therefore, MPC is an ideal candidate for solving the AUV motion control problems. On the other hand, since the optimization is solved by iterative numerical algorithms, the obtained control signal is an implicit function of the system state, which complicates the characterization of the closed-loop properties. Moreover, the nonlinear system dynamics makes the online optimization nonlinear programming (NLP) problems. The high computational complexity may cause an issue on the real-time control for embedded platforms with limited computing resources. In order to push the advanced MPC technology towards real-world AUV applications, this PhD dissertation is concerned with fundamental AUV motion control problems and attempts to address the aforementioned challenges and provide novel solutions. This dissertation proceeds with Chapter 1 by providing state-of-the-art introductions to related research areas. The mathematical model used for the AUV motion control is elaborated in Chapter 2. In Chapter 3, we consider the AUV navigation and control problem in constrained workspace. A unified receding horizon optimization framework consisting of the dynamic path planning and the nonlinear model predictive control (NMPC) tracking control is developed. Although the NMPC tracking controller well accommodates the practical constraints on the AUV system, it presents a brand new design philosophy compared with the existing control systems that are implemented on real AUVs. Since the existing AUV control systems are reliable controllers, AUV practitioners tend not to fully replace them but to improve the control performance by adding features. By considering this, in Chapter 4, we develop the Lyapunov-based model predictive control (LMPC) scheme which builds on the existing AUV control system and invoke online optimization to improve the control performance. Chapter 5 focuses on the path following (PF) problem. Unlike the trajectory tracking control which equally emphasizes the spatial and temporal control objectives, the PF control often prioritizes the path convergence over the speed assignment. To incorporate this objective prioritization into the controller design, a novel multi-objective model predictive control (MOMPC) scheme is developed. While the MPC technique provides several salient features (e.g., optimality, constraints handling, objective prioritization, robustness, etc.), those features come at a price: a computational bottleneck is formed by the heavy burden of solving online optimizations in real time. To explicitly address this issue, in Chapter 6, the computational complexity of the MPC algorithms is particularly emphasized. Two novel strategies which potentially alleviate the computational burden of the MPC-based AUV tracking control are proposed. In Chapter 7, some conclusive remarks are provided and a few avenues for future research are identified.
Author: Meiping Wu Publisher: Springer Nature ISBN: 9811694923 Category : Technology & Engineering Languages : en Pages : 3575
Book Description
This book includes original, peer-reviewed research papers from the ICAUS 2021, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2021 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.
Author: Sabiha Wadoo Publisher: CRC Press ISBN: 1351833928 Category : Technology & Engineering Languages : en Pages : 208
Book Description
Underwater vehicles present some difficult and very particular control system design problems. These are often the result of nonlinear dynamics and uncertain models, as well as the presence of sometimes unforeseeable environmental disturbances that are difficult to measure or estimate. Autonomous Underwater Vehicles: Modeling, Control Design, and Simulation outlines a novel approach to help readers develop models to simulate feedback controllers for motion planning and design. The book combines useful information on both kinematic and dynamic nonlinear feedback control models, providing simulation results and other essential information, giving readers a truly unique and all-encompassing new perspective on design. Includes MATLAB® Simulations to Illustrate Concepts and Enhance Understanding Starting with an introductory overview, the book offers examples of underwater vehicle construction, exploring kinematic fundamentals, problem formulation, and controllability, among other key topics. Particularly valuable to researchers is the book’s detailed coverage of mathematical analysis as it applies to controllability, motion planning, feedback, modeling, and other concepts involved in nonlinear control design. Throughout, the authors reinforce the implicit goal in underwater vehicle design—to stabilize and make the vehicle follow a trajectory precisely. Fundamentally nonlinear in nature, the dynamics of AUVs present a difficult control system design problem which cannot be easily accommodated by traditional linear design methodologies. The results presented here can be extended to obtain advanced control strategies and design schemes not only for autonomous underwater vehicles but also for other similar problems in the area of nonlinear control.
Author: Abul K. M. Azad Publisher: World Scientific ISBN: 9814415944 Category : Technology & Engineering Languages : en Pages : 904
Book Description
This book provides state-of-the-art scientific and engineering research findings and developments in the area of mobile robotics and associated support technologies. The book contains peer reviewed articles presented at the CLAWAR 2012 conference. Robots are no longer confined to industrial and manufacturing environments. A great deal of interest is invested in the use of robots outside the factory environment. The CLAWAR conference series, established as a high profile international event, acts as a platform for dissemination of research and development findings and supports such a trend to address the current interest in mobile robotics to meet the needs of mankind in various sectors of the society. These include personal care, public health, services in the domestic, public and industrial environments. The editors of the book have extensive research experience and publications in the area of robotics in general and in mobile robotics specifically, and their experience is reflected in editing the contents of the book.
Author: Thor I. Fossen Publisher: John Wiley & Sons ISBN: Category : Sports & Recreation Languages : en Pages : 504
Book Description
A comprehensive and extensive study of the latest research in control systems for marine vehicles. Demonstrates how the implementation of mathematical models and modern control theory can reduce fuel consumption and improve reliability and performance. Coverage includes ocean vehicle modeling, environmental disturbances, the dynamics and stability of ships, sensor and navigation systems. Numerous examples and exercises facilitate understanding.
Author: Jing Yan Publisher: Springer Nature ISBN: 9811660964 Category : Technology & Engineering Languages : en Pages : 222
Book Description
Autonomous underwater vehicles (AUVs) are emerging as a promising solution to help us explore and understand the ocean. The global market for AUVs is predicted to grow from 638 million dollars in 2020 to 1,638 million dollars by 2025 – a compound annual growth rate of 20.8 percent. To make AUVs suitable for a wider range of application-specific missions, it is necessary to deploy multiple AUVs to cooperatively perform the localization, tracking and formation tasks. However, weak underwater acoustic communication and the model uncertainty of AUVs make achieving this challenging. This book presents cutting-edge results regarding localization, tracking and formation for AUVs, highlighting the latest research on commonly encountered AUV systems. It also showcases several joint localization and tracking solutions for AUVs. Lastly, it discusses future research directions and provides guidance on the design of future localization, tracking and formation schemes for AUVs. Representing a substantial contribution to nonlinear system theory, robotic control theory, and underwater acoustic communication system, this book will appeal to university researchers, scientists, engineers, and graduate students in control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of AUVs. Moreover, the practical localization, tracking and formation schemes presented provide guidance on exploring the ocean. The book is intended for those with an understanding of nonlinear system theory, robotic control theory, and underwater acoustic communication systems.
Author: James William McMahon Publisher: ISBN: Category : Ocean engineering Languages : en Pages : 123
Book Description
In oceanic research and development, autonomous underwater vehicles (AUVs) provide scientists with the ability to augment expensive manned operations at a lower cost while simultaneously exploring regions that were previously inaccessible to scientists. While the cost of these AUVs is often nontrivial, the ability to autonomously sample data from varying regions over extended time periods removes the necessity of human operations which require much higher overhead costs. Scientists are now leveraging the unique abilities of AUVs to explore new environments, scientists are now starting to use AUVs to perform sophisticated missions in deep ocean environments, under the polar ice caps, or throughout dangerous minefields in the littoral. The success of these missions, however, depends on the ability of the AUV to autonomously perform complex tasks. Toward this goal, this dissertation seeks to enhance the capabilities of AUVs so that they are able to autonomously plan the high-level actions and the low-level motions needed to accomplish complex missions. A framework is developed which makes it possible to specify such missions in a structured language resembling English, and it automatically plans the actions and motions that the AUV needs to execute in order to accomplish the mission. The mission-specification language is grounded in well-established logical formalisms such as Regular Languages and Linear Temporal Logic. The inherent structure of the mission-specification language makes it possible to construct sophisticated mission such as exploring unknown areas, searching for objects of interest, or collecting data. In doing so, the framework alleviates the burden imposed on human operators who currently need to manually input highly detailed mission specifications into multiple configuration files, which increases the risk for mission failure due to human error. Instead, the framework makes it possible for the human operators to specify the missions in an easy-to-use, structured language. The technical contribution of the dissertation stems from a novel treatment of the combined mission and motion-planning problem as a hybrid search over discrete and continuous layers. Leveraging advances in AI and Robotics, a hybrid-planning framework is developed which combines high-level AI mission planning with low-level sampling-based motion planning. High-level planning, which operates over a discrete and abstract layer, breaks down the overall mission into a sequence of tasks. Sampling-based motion planning conducts a search over the feasible motions of the AUV in order to compute a trajectory that enables the AUV to accomplish each task. When sampling-based motion planning fails to make progress it requests another high-level plan from the AI planning layer. This interplay between high-level discrete planning and sampling-based motion planning is crucial to the success of the framework. The hybrid framework can be used with any AUV. Extensive experiments have been conducted with high-fidelity simulators and real AUVs, such as OceanServer Iver2 AUV and Reliant Bluefin-21 AUV. The experimental results show the ability of the approach to effectively plan collision-free and dynamically-feasible trajectories that enable the AUV to carry out sophisticated missions, such as inspection of numerous areas, data collection, and reacquisition and identification in Mine Countermeasures. The success of the hybrid framework highlight the potential of the approach to enhance the autonomy of AUVs, making it possible to carry out sophisticated missions at a lower operational cost.
Author: Charmane Venda Caldwell
Author: Charmane Venda Caldwell
Author: Yang Shi
Author: Chao Shen
Author: Meiping Wu
Author: Sabiha Wadoo
Author: Abul K. M. Azad
Author: Jian Zhou
Author: Thor I. Fossen
Author: Jing Yan
Author: James William McMahon