Model-based Cooperative Acoustic Navigation and Parameter Identification for Underactuated Underwater Vehicles

This thesis reports novel theoretical and experimental results addressing two increasingly important problems in underwater robotics: model-based cooperative acoustic navigation for underwater vehicles (UVs) lacking a Doppler velocity log (DVL) and dynamic-model parameter estimation for underactuated UVs, such as the now-ubiquitous class of torpedo-shaped UVs. This thesis reports an extension of a method to identify simultaneously UV dynamical plant model parameters (parameters for critical terms such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster model) in 6 degrees of freedom (DOF) to tolerate simulated sensor measurement noise representative of representative of real-world sensor data, as well as extensive numerical simulations to evaluate the sensitivity of the approach to sensor noise. The current state-of-the-art in one-way travel time (OWTT) combined acoustic communication and navigation (cooperative acoustic navigation) is to utilize purely kinematic, constant-velocity plant process models together with an on-board bottom-lock DVL to provide frequent, high-accuracy velocity corrections. However, DVLs are expensive, power consumers, physically large, and limited to acoustic bottom-lock range, which restricts their use to O(10-100m) above the sea floor or beneath surface ice. Simulation and experimental results reported herein indicate the submerged UV position estimate from cooperative acoustic navigation with a kinematic model is poor and even unstable in the absence of DVL velocity observations. These simulation and experimental results also show that cooperative acoustic navigation with a dynamic plant model performs well without a DVL and outperforms DVL-based dead reckoning, at least in the situation presented herein where the magnitude of the ambient water-current velocity is small. The performance of the UV dynamic model, i.e., its ability to predict the vehicle's state, depends primarily on the accuracy of the model structure and model parameters. Accurate estimates of these parameters are also required for model-based control, fault detection, and simulation of UV. While the general form of dynamical plant models for UVs is well understood, accurate values for dynamic-model parameters are impossible to determine analytically, are not provided by UV manufacturers, and can only be determined experimentally. Moreover, oceanographic UVs are subject to frequent changes in physical configuration, including changes in ballasting and trim, on-board equipment, and instrumentation (both external and internal), which may significantly affect the vehicle dynamics. Plant-model parameter estimation is generally more difficult for underactuated, torpedo-shaped UVs than for fully actuated UVs with thrusters because: 1) the reduced actuation available on underactuated UV limits the plant excitation that can be induced from the control inputs, and 2) torpedo-shaped vehicles are often actuated with control surfaces (e.g., fins, wings, rudders, etc), which are difficult to characterize independently of the plant model parameters. For these reasons, we seek an approach to parameter estimation for underactuated UVs in 6 DOF that simultaneously estimates plant and actuator parameters and can be performed routinely in the field with minimal time and effort by the vehicle operator. The goals of this thesis are to advance the state-of-the-art of (1) model-based state estimation for cooperative acoustic navigation of UVs and (2) dynamic plant-model parameter identification for underactuated UVs. The first goal is addressed with the evaluation of a dynamic UV plant model in cooperative acoustic navigation and a comparative analysis of the dynamic UV model and kinematic UV model without a DVL. The second goal is addressed in a collaborative effort comprising: (1) the development of the nullspace-based least squares (NBLS) algorithm for underactuated UV plant-parameter and actuator-parameter estimation in 6 DOF, and (2) the extension of an AID algorithm, and corresponding stability proof, to estimate simultaneously plant-model and actuator parameters for underactuated UVs with diagonal mass and drag matrices in 6 DOF with realistic sensor measurement noise. These capabilities were verified by in situ vehicle experiments with the JHU Iver3 AUV and by simulation studies

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

University of Maine Undergraduate Catalog, 2022-2023

The University of Maine undergraduate catalog for the 2022-2023 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture