Intelligent Marine and Aerial Vehicles: Theory and Applications


Meng Joo Er (Editor)
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Ning Wang (Editor)
School of Marine Electrical Engineering, Dalian Maritime University, Dalian, PR China

Mahardhika Pratama (Editor)
SCES, Nanyang Technological University, Nanyang Avenue, Singapore

Sanjay Sharma (Editor)
School of Engineering, Plymouth University, Plymouth, UK

Zhichao Lian (Editor)
College of Computer Science and Engineering, Nanjing University of Science and Technology, China

Series: Robotics Research and Technology
BISAC: TEC060000

Unmanned marine vehicles (UMVs) is a collective term commonly used to describe autonomous underwater vehicles, remotely operated vehicles, semi-submersibles, and unmanned surface craft. UMVs are heavily used in the military, civilian, and scientific communities for undertaking designated missions whilst either operating autonomously and/or in co-operation with other types of vehicles. Advanced marine vehicles are increasing their capabilities and the degree of autonomy more and more in order to perform more sophisticated maritime missions. Remotely operated vehicles are no longer cost-effective since they are limited by economic support costs, and the presence and skills of the human operator. Alternatively, autonomous surface and underwater vehicles have the potential to operate with greatly reduced overhead costs and level of operator intervention.

An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. UAVs are a component of an unmanned aircraft system (UAS); these include a UAV, a ground-based controller, and a system of communications between the two. Compared to manned aircraft, UAVs were originally used for missions too “dull, dirty or dangerous” for humans. While they originated mostly in military applications, their use is rapidly expanding to commercial, scientific, recreational, agricultural, and other applications such as policing, peacekeeping and surveillance, product deliveries, aerial photography, agriculture, smuggling, and drone racing. Civilian UAVs now vastly outnumber military UAVs, with estimates of over a million sold by 2015, so they can be seen as an early commercial application of Autonomous Things, to be followed by the autonomous car and home robots.

Nowadays, UMVs and UAVs are playing an increasingly important role in both controlling community and engineering applications. For example, UMVs and UAVs provide more efficient ways to execute various challenging tasks. However, these systems are usually featured with dynamics coupling, actuator saturation, underactuated structure, time-varying disturbance, etc., thereby resulting in great challenges and difficulties in system analysis and controller design. Recently, by employing intelligent approaches, advanced control methodologies for unmanned systems have been rapidly developed. Note that the dynamic environment is usually changing and the unmanned systems must adapt themselves accordingly. In this context, on one hand, more efforts should be focused on the methodology of the learning system. For example, fast adaptation and self-organizing capability are essentially required. On the other hand, advanced analysis tools should be deployed to enhance the control performance. Towards this end, human-like intelligence should be integrated tightly with nonlinear design for complex control tasks of autonomous systems.

The main objective of this edited book is to address various challenges and issues pertinent to the intelligent control of UMVs and UAVs.

Table of Contents

Table of Contents


Chapter 1. Autonomous Underwater Vehicles: A Review
(Meng Joo Er and Rajasekar Venkatesan, School of EEE, Nanyang Technological University, Singapore)

Chapter 2. Intelligent Control of Unmanned Marine Vehicles with Input Nonlinearities and Unknown Disturbances: A Brief Review
(Xiaozhao Jin, Ning Wang , Meng Joo Er and Zhuo Sun, School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China, and others)

Chapter 3. Unmanned Marine Vehicles Modeling and Control Using Generalized Ellipsoidal Basis Function-Based Fuzzy Neural Networks
(Ning Wang, Shuailin Lv, and MengJoo Er, Marine Engineering College, Dalian Maritime University, Dalian, China, and others)

Chapter 4. Autonomous Underwater Vehicles
(Osama Hassanein, Sreenatha G. Anavatti, Mahardhika Pratama and Tapabrata Ray, Abu Dhabi polytechnic, ADPoly, IAT, Abu Dhabi, UAE, and others)

Chapter 5. Probabilistic, Autonomous Detection and Tracking of Subsea Cables and Pipelines by an Unmanned Surface Vehicle
(T. Szyrowski, A. Motwani, K. S. Sharma, and R. Sutton, School of Marine Science and Engineering, Plymouth University, Plymouth, UK, and others)

Chapter 6. Constrained Estimation–Based Nonlinear Aerial Contact Predictive Control
(Basaran Bahadir Kocer, Gerald Seet Gim Lee, and Tegoeh Tjahjowidodo, Faculty of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore)

Chapter 7. Fuzzy Systems for Modelling and Control in Aerial Robotics
(Fendy Santoso, Matthew A. Garratt, and Sreenatha G. Anavatti, School of Engineering and Information Technology, University of New South Wales, Canberra, Australia)

Chapter 8. Discriminative Object Tracking Methods
(Zhichao Lian, Zhonggen Liu and Changju Feng, College of Computer Science and Engineering, Nanjing University of Science and Technology, China)


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