This book presents an Intelligent Control Architecture (ICA) to enable multiple collaborating marine vehicles to autonomously carry out underwater intervention missions. The presented ICA is generic in nature but aimed at a case study where a marine surface craft and an underwater vehicle are required to work cooperatively. It is shown that they are capable of cooperating autonomously towards the execution of complex activities since they have different but complementary capabilities. The ICA implementation is verified in simulation, and validated in trials by means of a team of autonomous marine robots. This book also presents architectural details and evaluation scenarios of the ICA, results of simulations and trials from different maritime operations, and future research directions.

Autonomy for Marine Robots provides a timely and insightful overview of intelligent autonomy in marine robots. A brief history of this emerging field is provided, along with a discussion of the challenges unique to the underwater environment and their impact on the level of intelligent autonomy required. Topics covered at length examine advanced frameworks, path-planning, fault tolerance, machine learning, and cooperation as relevant to marine robots that need intelligent autonomy.

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. (Nova)

Autonomous vehicles (AVs) have been used in military operations for more than 60 years, with torpedoes, cruise missiles, satellites, and target drones being early examples.1 They have also been widely used in the civilian sector-for example, in the disposal of explosives, for work and measurement in radioactive environments, by various offshore industries for both creating and maintaining undersea facilities, for atmospheric and undersea research, and by industry in automated and robotic manufacturing. Recent military experiences with AVs have consistently demonstrated their value in a wide range of missions, and anticipated developments of AVs hold promise for increasingly significant roles in future naval operations. Advances in AV capabilities are enabled (and limited) by progress in the technologies of computing and robotics, navigation, communications and networking, power sources and propulsion, and materials. Autonomous Vehicles in Support of Naval Operations is a forward-looking discussion of the naval operational environment and vision for the Navy and Marine Corps and of naval mission needs and potential applications and limitations of AVs. This report considers the potential of AVs for naval operations, operational needs and technology issues, and opportunities for improved operations.

Robotic marine vessels can be used for a wide range of purposes, including defence, marine science, offshore energy and hydrographic surveys, and environmental surveys and protection. Such vessels need to meet a variety of criteria: they must be able to operate in salt water, and to communicate and be controlled over large distances, even when submerged or in inclement weather. Further challenges include 3D navigation of individual vehicles, groups or squadrons. This book covers the current state of research in navigation, modelling and control of marine autonomous vehicles, and deals with various related topics, including collision avoidance, communication, and a range of applications. It provides valuable insights for an audience of researchers, academics and postgraduate students interested in autonomous marine vessels, robotics, and electrical and automobile engineering.

Unmanned marine vehicles (UMVs) include autonomous underwater vehicles, remotely operated vehicles, semi-submersibles and unmanned surface craft. Considerable importance is being placed on the design and development of such vehicles, as they provide cost-effective solutions to a number of littoral, coastal and offshore problems. This book highlights the advanced technology that is evolving to meet the challenges being posed in this exciting and growing area of research.

Intelligent Autonomy (IA) is a multi-year program within the Office of Naval Research (ONR) Autonomous Operations (AO) Future Naval Capabilities (FNC) program. The primary goal of the effort is to develop and demonstrate technologies for highly automated and fully autonomous mission planning and dynamic re-tasking of multiple classes of Naval unmanned systems and minimization of human intervention in unmanned vehicle operations. This technology is being applied to both individual and teams of unmanned air, surface, ground, and undersea vehicles for a variety of mission areas including reconnaissance/search, persistent surveillance, tracking, and some limited application to strike. Autonomy technologies will be matured through a series of phased demonstrations to allow low risk transition to current and future Navy and Marine Corps systems. Demonstrations will be done using both real vehicles and simulation. Some of the major simulation demonstrations will be done within the context of a simulated warfare environment at the Naval Air Systems Command based around the Air Combat Environment Test and Evaluation Facility (ACETEF) and the Unmanned System Research and Development Lab (USRDL). The demonstrations at NAVAIR will utilize much of the architecture and many of the assets from the NCW4.0X Virtual Laboratory (V-LAB) project. Metrics for testing of IA software in this environment are currently being developed. This paper will discuss some candidate performance metrics that are currently being considered for evaluation of the Intelligent Autonomy technologies.