Design of Large Diameter Mine Countermeasure Hybrid Power Unmanned Underwater Vehicle

Mines are one of the most cost-effective and moderated weapon systems that are easy to deploy, but difficult to clear. Not only has the development of the mine countermeasure (MCM) underwater unmanned vehicle (UUV) improved cost- and time-effectiveness in operation, but also it has avoided unnecessary human casualties

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Biologically inspired learning system

Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based supervisory mechanism. Four main learning algorithms have been developed, along with a goal directed random exploration routine. These algorithms all use the concept of learning from a recent memory in order to save the robot/agent from having to exhaustively execute all trial solutions. The first algorithm is a reactive online learning algorithm that uses a supervised learning to find the sensor/action combinations that promote realization of a preprogrammed goal. It produces a feed forward neural network controller that is used to control the robot. The second algorithm is similar to first in that it uses a supervised learning strategy, but it produces a neural network that considers past values, thus providing a non-reactive solution. The third algorithm is a departure from the first two in that uses a non-supervised learning technique to learn the best actions for each situation the robot encounters. The last algorithm builds a graph of the situations encountered by agent/robot in order to learn to associate the best actions with sensor inputs. It uses an unsupervised learning approach based on shortest paths to a goal situation in the graph in order to generate a non-reactive feed forward neural network. Test results were good, the first and third algorithms were tested in a formation maneuvering task in both simulation and onboard mobile robots, while the second and fourth were tested simulation

Pose Detection and Control of Unmanned Underwater Vehicles (UUVs) Utilizing an Optical Detector Array

As part of the research for development of a leader-follower formation between unmanned underwater vehicles (UUVs), this study presents an optical feedback system for UUV navigation via an optical detector array. Capabilities of pose detection and control in a static-dynamic system (e.g. UUV navigation into a docking station) and a dynamic-dynamic system (e.g. UUV to UUV leader-follower system) are investigated. In both systems, a single light source is utilized as a guiding beacon for a tracker/follower UUV. The UUV uses an optical array consisting of photodiodes to receive the light field emitted from the light source. For UUV navigation applications, accurate pose estimation is essential. In order to evaluate the feasibility of underwater distance detection, the effective communication range between two platforms, i.e. light source and optical detector, and the optimum spectral range that allowed maximum light transmission are calculated. Based on the light attenuation in underwater, the geometry and dimensions of an optical detector array are determined, and the boundary conditions for the developed pose detection algorithms along with the error sources in the experiments are identified. As a test bed to determine optical array dimensions and size, a simulator, i.e. numerical software, is developed, where planar and curved array geometries of varying number of elements are analytically compared and evaluated. Results show that the curved optical detector array is able to distinguish 5 degree- of-freedom (DOF) motion (translation in x, y, z-axes and pitch and yaw rotations) with respect to a single light source. Analytical pose detection and control algorithms are developed for both static-dynamic and dynamic-dynamic systems. Results show that a 5 x 5 curved detector array with the implementation of SMC is reasonably sufficient for practical UUV positioning applications. The capabilities of an optical detector array to determine the pose of a UUV in 3-DOF (x, y and z-axes) are experimentally tested. An experimental platform consisting of a 5 x 5 photodiode array mounted on a hemispherical surface is used to sample the light field emitted from a single light source. Pose detection algorithms are developed to detect pose for steady-state and dynamic cases. Monte Carlo analysis is conducted to assess the pose estimation uncertainty under varying environmental and hardware conditions such as water turbidity, temperature variations in water and electrically-based noise. Monte Carlo analysis results show that the pose uncertainties (within 95% confidence interval) associated with x, y and z-axes are 0.78 m, 0.67 m and 0.56 m, respectively. Experimental results demonstrate that x, y and z-axes pose estimates are accurate to within 0.5 m, 0.2 m and 0.2 m, respectively

Reference Model for Interoperability of Autonomous Systems

This thesis proposes a reference model to describe the components of an Un-manned Air, Ground, Surface, or Underwater System (UxS), and the use of a single Interoperability Building Block to command, control, and get feedback from such vehicles. The importance and advantages of such a reference model, with a standard nomenclature and taxonomy, is shown. We overview the concepts of interoperability and some efforts to achieve common refer-ence models in other areas. We then present an overview of existing un-manned systems, their history, characteristics, classification, and missions. The concept of Interoperability Building Blocks (IBB) is introduced to describe standards, protocols, data models, and frameworks, and a large set of these are analyzed. A new and powerful reference model for UxS, named RAMP, is proposed, that describes the various components that a UxS may have. It is a hierarchical model with four levels, that describes the vehicle components, the datalink, and the ground segment. The reference model is validated by showing how it can be applied in various projects the author worked on. An example is given on how a single standard was capable of controlling a set of heterogeneous UAVs, USVs, and UGVs

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

China Maritime Report No. 29: PLAN Mine Countermeasures: Platforms, Training, and Civil-Military Integration

The People’s Liberation Army Navy (PLAN) has made incremental progress in its mine countermeasures (MCM) program in recent years. The PLAN’s current inventory of about 60 MCM ships and craft includes classes of minehunters and minesweepers mostly commissioned in the past decade as well as unmanned surface vessels (USVs) and remotely operated vehicles with demonstrated explosive neutralization capability. Despite the addition of these advanced MCM platforms and equipment, experts affiliated with the PLAN and China’s mine warfare development laboratory have serious reservations about the PLAN’s current ability to respond to the full range of likely threats posed by naval mines in future contingencies. The PLAN’s MCM forces are currently organized for operations near China’s coastline, but writings by Chinese military and civilian experts contend that to safeguard Beijing’s expanding overseas interests, the PLAN must develop MCM capabilities for operations far beyond the First Island Chain. PLAN and civilian mine warfare experts have proposed various solutions for offsetting perceived shortcomings in the PLAN’s MCM program, including the development of autonomous USVs and unmanned underwater vehicles (UUVs), deployment of modularized MCM mission packages on ships such as destroyers and frigates, and mobilization of civilian assets such as ships and helicopters in support of MCM operations. Although there appears to have been little to no adoption of these proposed solutions to date, the PLAN recognizes MCM as one of its biggest challenges, and one can expect the PLAN to continue making measured progress in its MCM program in the years ahead.https://digital-commons.usnwc.edu/cmsi-maritime-reports/1028/thumbnail.jp