The overall objective of this work is to evaluate the ability of homing and docking an unmanned underwater vehicle (Hydroid REMUS 100 UUV) to a moving unmanned surface vehicle (Wave-Adaptive Modular Surface Vehicle USV) using a Hydroid Digital Ultra-Short Baseline (DUSBL) acoustic positioning system (APS), as a primary navigation source. An understanding of how the UUV can rendezvous with a stationary USV first is presented, then followed by a moving USV. Inherently, the DUSBL-APS is susceptible to error due to the physical phenomena of the underwater acoustic channel (e.g. ambient noise, attenuation and ray refraction). The development of an APS model has allowed the authors to forecast the UUV’s position and the estimated track line of the USV as determined by the DUSBL acoustic sensor. In this model, focus is placed on three main elements: 1) the acoustic channel and sound ray refraction when propagating in an in-homogeneous medium; 2) the detection component of an ideal DUSBL-APS using the Neyman-Pearson criterion; 3) the signal-to-noise ratio (SNR) and receiver directivity impact on position estimation. The simulation tool is compared against actual open water homing results in terms of the estimated source position between the simulated and the actual USBL range and bearing information. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Este trabalho estuda o sistema de controle de um robô móvel, termo que designa veículos sem motorista humano e com capacidade de trafegar por rotas livremente escolhidas. As arquiteturas de controle utilizadas em robôs móveis são analisadas. A arquitetura adotada neste trabalho, de caráter funcional,é apresentada e discutida. O trabalho se concentra nos níveis mais simples de controle, relacionados ao controle de trajetória, cujo objetivo é garantir que o robô móvel percorra uma rota pré-definida. Um controlador de trajetória é proposto e projetado. O controlador resulta da aplicação da teoria de controle ótimo a um modelo de robô móvel em referencial fixo. Uma técnica recente de controle de robôs (técnica de atgs) é empregada para melhorar a robustez do controlador. O desempenho do controlador obtido com uso de atgs é comparado com o desempenho do controlador obtido inicialmente. Com o objetivo de validar esta proposta de controlador de trajetória, resultados fornecidos por simulações são discutidos. A análise do controlador foi respaldada com dados experimentais obtidos junto a um robô móvel, denominado Ariel, desenvolvido no laboratório de automação e sistemas (mecatrônica) da Escola Politécnica da Universidade de São Paulo. / This work studies the Control System of a Mobile Robot, term which refers to vehicles without human driver and with ability to follow arbitrary routes. This work analyses the Control Architectures frequently employed in Mobile Robots. The Architecture here adopted is a functional one, which is presented and described. This work focuses on the simplest levels of Control, those which are mainly related to the Trajectory Control, and whose objective is to guarantee that the Mobile Robot follows a specified route. A Trajectory Controller is proposed and designed. The Controller is based on Optimal Control Theory. A recently developed technique for robot control (called ATGS techinique) is used in order to improve the Controller robustness. Simulation results are discussed in order to validate the proposed Controller. The Controller analysis is tested in a real Mobile Robot (named Ariel) currently developed at Laboratório de Automação e Sistemas (Mecatrônica) , at Escola Politécnica of Universidade de São Paulo.

Este trabalho propõe um Ambiente de Programação de Robôs Móveis direcionado especialmente à língua portuguesa, que consiste em um novo ambiente de programação gráfica e textual, capaz de atender a perfis diferenciados de usuários. O ambiente de programação contém três módulos para programação de robôs móveis: o modulo C ou editor C, o módulo Assembly ou editor Assembly, e o módulo blocos que é um módulo que contêm blocos de programação para auxiliar os programadores inexperientes a programarem robôs móveis por meio de uma meta linguagem desenvolvida que encapsula a linguagem C. O diferencial deste trabalho consiste na Gramática adaptativa criada para robôs móveis, que é uma derivação das gramáticas descendentes recursivas com um mecanismo de busca (\"look ahead\"). A derivação encontra-se na definição formal de gramática que foi adaptada nessa proposta para permitir a categorização de terminais da gramática, adicionando um passo a mais na compilação, que é a checagem de categorias permitidas da linguagem. Nesta dissertação são relacionados alguns ambientes de programação de robôs disponíveis na literatura bem como uma discussão de suas características. Também são apresentados experimentos realizados com usuários não especializados em programação, principalmente crianças, e com o Laboratório de Robótica a Distância da Universidade de São Paulo em São Carlos, São Paulo / This work proposes a Mobile Robot Programming Environment focused in the Portuguese language, which is able to attend different user categories and where the user can work textually and graphically. The programming environment contains three different modules: the C module, or C editor; the Assembly module, or Assembly editor; and the block module, which contains programming blocks that can help novice programmers to develop software for mobile robots using a meta-language that encapsulates the C language. The major contribution of this work consists of the proposition of a novel adaptive grammar, specially developed to mobile robots, which is a derivation of recursive descendent grammars, containing a look ahead module. The derivation is found in the formal definition of the grammar, adapted to allow the specification of grammar terminals, adding one more compilation step, which is the check of language permitted categories. The document presents some robot programming environments found in literature and discusses their characteristics. The results show experiments performed with non-specialized users, mostly children, and with the Remote Robotics Laboratory of the University of São Paulo, in São Carlos, São Paulo state, Brazil

Localization is one of the most difficult and costly problems in mobile robotics. Vision and odometry/AHRS (Attitude and Heading Reference System, three axial gyroscopes, accelerometers and magnetometers) sensors fusion strategy is prevalent in the recent years for the robot localization, due to its low cost and effectiveness in GPS-denied environments. In this thesis, a new adaptive estimation algorithm is proposed to estimate the robot position by fusing the monocular vision and odometry/AHRS sensors, and utilizing the properties of perspective projection. By the new method, the robot can be localized in real time in the GPS-denied and mapless environments, and the localization results can be theoretically proved convergent to their real values. Compared to other methods, our algorithm is simple to implement and suitable for parallel processing. To achieve the real-time performance, the algorithm is implemented in parallel using GPU (Graphics Processing Unit), and therefore it can be easily integrated into mobile robots’ tasks like navigation and motion control, which need the real-time localization information. Simulations and experiments were conducted to validate the good convergence and longtime robustness performances of the proposed real-time localization algorithm. / With the developed vision based localization method as a position estimator, a new controller for trajectory tracking of the non-holonomic wheeled robot is proposed without direct position measurement. The nonholonomic motion constraint of mobile robots is fully taken into account, compared to most of existing visual sevo controllers for mobile robots. It is proved by Lyapunov theory that the proposed adaptive visual servo controller for the wheeled robot gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. Experiments on a wheeled robot are conducted to validate the effectiveness and robust performance of the proposed controller. / Adopting the similar idea, the new vision based localization method is once again embedded into a trajectory tracking controller for the underactuated water surface robot. It is proved once again by Lyapunov theory that the proposed adaptive visual servo controller for the underactuated water surface robot gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. Experiments are conducted on an underactuated water surface robot to validate the effectiveness and robust performance of the proposed controller. / The contribution of this thesis can be summarized as follows: firstly, a novel localization algorithm based on the fusion of the monocular vision and AHRS/odometry sensors is proposed. Secondly, with the former localization method embedded as a position estimator, a new controller for visually servoed trajectory tracking of the nonholonomic wheeled robot is developed. Finally, by adopting the similar strategy, this thesis proposes a new controller for visually servoed trajectory tracking of the underactuated water surface robot without direct position measurement. / 定位是移動機器人中最困難和花費最高的問題之一。由於其低成本和在無GPS（全球定位系統）環境中的有效性，視覺和里程計/ AHRS（姿態航向參考系統，三軸陀螺儀，加速度計和磁力計）傳感器融合是近年來流行的機器人定位策略。這篇論文提出了一種新的自適應估計算法，融合單目視覺和里程計/ AHRS 傳感器，並利用透視投影的特性來估計機器人位置。利用這種新方法，機器人可以實時地在無GPS 和無地圖的環境中被定位，而且定位結果可從理論上證明收斂到他們的真實值。與其它方法相比，我們的算法很容易實現，並適於並行處理。為了得到實時性能，算法是用GPU（圖形處理單元）來並行實現的，因此它可以很容易地集成到移動機器人需要實時定位信息的任務，如導航和運動控制。仿真和實驗驗證了我們的實時定位算法具有很好的收斂及長時間的魯棒表現。 / 利用上述基於視覺的定位方法作為位置估計器，我們為一階非完整移動機器人的軌跡跟踪提出了一種新的、不直接依賴位置測量的控制器。相比於大多數現有的用於移動機器人的視覺伺服控制器，我們的方法充分考慮了移動機器人的非完整運動約束。我們通過Lyapunov穩定性理論證明了本論文所提出的自適應視覺伺服控制器可以保證一階非完整移動機器人對理想軌跡的跟蹤，並且被估計的機器人位置會漸近收斂到其實際的位置。我們在輪式機器人上進行了相應的實驗，驗證了本論文所提出的控制器的有效性和魯棒性。 / 採用類似的思路，這種基於視覺的定位方法被再次嵌入到二階非完整移動機器人（欠驅動水面機器人）的軌跡跟踪控制器。我們再一次通過Lyapunov穩定性理論證明了本論文所提出的自適應視覺伺服控制器可以保證二階非完整移動機器人對理想軌跡的跟蹤，並且被估計的機器人位置會漸近收斂到其iv實際的位置。我們在欠驅動水面機器人上進行了相應的實驗，驗證了本論文所提出的控制器的有效性和魯棒性。 / 這篇論文的貢獻可以歸納如下：首先，基於單目視覺和AHRS/測距傳感器的融合，我們提出了一種新的定位算法。其次，通過將上述基於視覺的定位方法內嵌為位置估計器，我們為一階非完整移動機器人（輪式機器人）設計了一種新的基於視覺伺服的軌跡跟踪控制器。最後，通過採用類似的避免機器人位置測量的策略，本文為二階非完整移動機器人（欠驅動水面機器人）設計了一種新的基於視覺伺服的軌跡跟踪控制器。 / Wang, Kai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 93-100). / Abstracts also in Chinese. / Title from PDF title page (viewed on 20, December, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Lo Wang Tai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 58-63). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Tables of Content --- p.iv / List of Figures --- p.vii / List of Tables --- p.viii / Chapter Chapter1 --- Introduction --- p.1 / Chapter 1.1 --- Internet-based Tele-cooperation --- p.1 / Chapter 1.1.1 --- Cooperative Control of Multiple Robot --- p.1 / Chapter 1.1.2 --- Internet-based Teleoperation --- p.3 / Chapter 1.1.3 --- Time Delay of Internet Communication --- p.4 / Chapter 1.2 --- Related Work --- p.5 / Chapter 1.3 --- Motivation and Contribution --- p.6 / Chapter 1.3.1 --- Motivation --- p.6 / Chapter 1.3.2 --- Contribution --- p.7 / Chapter 1.4 --- Outline of the thesis --- p.8 / Chapter Chapter2 --- The Internet Robotic System --- p.9 / Chapter 2.1 --- System Architecture --- p.9 / Chapter 2.2 --- The Hardware --- p.12 / Chapter 2.2.1 --- Operator System --- p.12 / Chapter 2.2.2 --- Mobile Robot System --- p.13 / Chapter 2.2.3 --- Multi-fingered Robot Hand System --- p.17 / Chapter 2.2.4 --- Visual Tracking System --- p.19 / Chapter 2.3 --- Software Design --- p.21 / Chapter 2.3.1 --- Robot Client and Arm Client --- p.22 / Chapter 2.3.2 --- Robot Server --- p.23 / Chapter 2.3.3 --- Image Server --- p.25 / Chapter 2.3.4 --- Arm Server --- p.75 / Chapter 2.3.5 --- Arm Controller --- p.27 / Chapter 2.3.6 --- Finger Server --- p.27 / Chapter 2.3.7 --- Finger Controller --- p.27 / Chapter 2.3.8 --- Robot Tracker --- p.28 / Chapter 2.3.9 --- Interaction Forwarder --- p.28 / Chapter Chapter3 --- Event-based Control for Force Reflecting Teleoperation --- p.29 / Chapter 3.1 --- Modeling and Control --- p.29 / Chapter 3.1.1 --- Model of Operator System --- p.31 / Chapter 3.1.2 --- Model of Mobile Robot System --- p.33 / Chapter 3.1.3 --- Model of Multi-fingered Hand System --- p.34 / Chapter 3.2 --- Force Feedback Generation --- p.35 / Chapter 3.2.1 --- Obstacle Avoidance --- p.35 / Chapter 3.2.2 --- Singularity Avoidance --- p.38 / Chapter 3.2.3 --- Interaction Rendering --- p.40 / Chapter Chapter4 --- Experiments --- p.42 / Chapter 4.1 --- Experiment1 --- p.42 / Chapter 4.2 --- Experiment2 --- p.47 / Chapter 4.3 --- Experiment3 --- p.52 / Chapter Chapter5 --- Future Wok --- p.54 / Chapter Chapter6 --- Conclusions --- p.56 / Bibliography --- p.58

"Recent works on the control of wheeled mobile robots have shifted from the use of the kinematic model to the use of the dynamic model. Since theoretical results typically treat the inputs to the dynamic model as torques, few experimental results have been provided, as torque is typically not the input to most commercially available robots. Few papers have implemented controllers based on the dynamic model, and those that have did not address the issue of identifying the parameters of the dynamic model. This work focuses on a method for identifying the parameters of the dynamic model of a wheeled mobile robot. The method is shown to be both effective and easy to implement, and requires no prior knowledge of what the parameters may be. Experimental results on two mobile robots of different scale demonstrate its effectiveness. The estimates of the parameters created by the proposed method are then used in an adaptive controller to verify their accuracy. For future work, this method should be completed autonomously in a two-part manner, onboard the mobile robot. First, the robot should perform the method proposed here to generate an initial parameter estimate, and then use adaptive control to update the estimates."

by Law Kwok Ho Cedric. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 106-112). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Related work --- p.4 / Chapter 1.3 --- Thesis overview --- p.5 / Chapter 2 --- Single wheel robot: Gyrover --- p.9 / Chapter 2.1 --- Background --- p.9 / Chapter 2.2 --- Robot concept --- p.11 / Chapter 2.3 --- System description --- p.14 / Chapter 2.4 --- Flywheel characteristics --- p.16 / Chapter 2.5 --- Control patterns --- p.20 / Chapter 3 --- Learning Control --- p.22 / Chapter 3.1 --- Motivation --- p.22 / Chapter 3.2 --- Cascade Neural Network with Kalman filtering --- p.24 / Chapter 3.3 --- Learning architecture --- p.27 / Chapter 3.4 --- Input space --- p.29 / Chapter 3.5 --- Model evaluation --- p.30 / Chapter 3.6 --- Training procedures --- p.35 / Chapter 4 --- Control Architecture --- p.38 / Chapter 4.1 --- Behavior-based approach --- p.38 / Chapter 4.1.1 --- Concept and applications --- p.39 / Chapter 4.1.2 --- Levels of competence --- p.44 / Chapter 4.2 --- Behavior-based control of Gyrover: architecture --- p.45 / Chapter 4.3 --- Behavior-based control of Gyrover: case studies --- p.50 / Chapter 4.3.1 --- Vertical balancing --- p.51 / Chapter 4.3.2 --- Tiltup motion --- p.52 / Chapter 4.4 --- Discussions --- p.53 / Chapter 5 --- Implement ation of Learning Control --- p.57 / Chapter 5.1 --- Validation --- p.57 / Chapter 5.1.1 --- Vertical balancing --- p.58 / Chapter 5.1.2 --- Tilt-up motion --- p.62 / Chapter 5.1.3 --- Discussions --- p.62 / Chapter 5.2 --- Implementation --- p.65 / Chapter 5.2.1 --- Vertical balanced motion --- p.65 / Chapter 5.2.2 --- Tilt-up motion --- p.68 / Chapter 5.3 --- Combined motion --- p.70 / Chapter 5.4 --- Discussions --- p.72 / Chapter 6 --- Shared Control --- p.74 / Chapter 6.1 --- Concept --- p.74 / Chapter 6.2 --- Schemes --- p.78 / Chapter 6.2.1 --- Switch mode --- p.79 / Chapter 6.2.2 --- Distributed mode --- p.79 / Chapter 6.2.3 --- Combined mode --- p.80 / Chapter 6.3 --- Shared control of Gyrover --- p.81 / Chapter 6.4 --- How to share --- p.83 / Chapter 6.5 --- Experimental study --- p.88 / Chapter 6.5.1 --- Heading control --- p.89 / Chapter 6.5.2 --- Straight path --- p.90 / Chapter 6.5.3 --- Circular path --- p.91 / Chapter 6.5.4 --- Point-to-point navigation --- p.94 / Chapter 6.6 --- Discussions --- p.95 / Chapter 7 --- Conclusion --- p.103 / Chapter 7.1 --- Contributions --- p.103 / Chapter 7.2 --- Future work --- p.104

Este trabalho estuda o sistema de controle de um robô móvel, termo que designa veículos sem motorista humano e com capacidade de trafegar por rotas livremente escolhidas. As arquiteturas de controle utilizadas em robôs móveis são analisadas. A arquitetura adotada neste trabalho, de caráter funcional,é apresentada e discutida. O trabalho se concentra nos níveis mais simples de controle, relacionados ao controle de trajetória, cujo objetivo é garantir que o robô móvel percorra uma rota pré-definida. Um controlador de trajetória é proposto e projetado. O controlador resulta da aplicação da teoria de controle ótimo a um modelo de robô móvel em referencial fixo. Uma técnica recente de controle de robôs (técnica de atgs) é empregada para melhorar a robustez do controlador. O desempenho do controlador obtido com uso de atgs é comparado com o desempenho do controlador obtido inicialmente. Com o objetivo de validar esta proposta de controlador de trajetória, resultados fornecidos por simulações são discutidos. A análise do controlador foi respaldada com dados experimentais obtidos junto a um robô móvel, denominado Ariel, desenvolvido no laboratório de automação e sistemas (mecatrônica) da Escola Politécnica da Universidade de São Paulo. / This work studies the Control System of a Mobile Robot, term which refers to vehicles without human driver and with ability to follow arbitrary routes. This work analyses the Control Architectures frequently employed in Mobile Robots. The Architecture here adopted is a functional one, which is presented and described. This work focuses on the simplest levels of Control, those which are mainly related to the Trajectory Control, and whose objective is to guarantee that the Mobile Robot follows a specified route. A Trajectory Controller is proposed and designed. The Controller is based on Optimal Control Theory. A recently developed technique for robot control (called ATGS techinique) is used in order to improve the Controller robustness. Simulation results are discussed in order to validate the proposed Controller. The Controller analysis is tested in a real Mobile Robot (named Ariel) currently developed at Laboratório de Automação e Sistemas (Mecatrônica) , at Escola Politécnica of Universidade de São Paulo.

The PSUBOT (pronounced pea-es-you-bought) is an autonomous wheelchair robot for persons with certain disabilities. Its use of voice recognition and autonomous navigation enable it to carry out high level commands with little or no user assistance. We first describe the goals, constraints, and capabilities of the overall system including path planning and obstacle avoidance. We then focus on localization-the ability of the robot to locate itself in space. Odometry, a compass, and an algorithm which matches points to lines are each employed to accomplish this task. The matching algorithm (which matches "points" to "lines") is the main contribution to this work. The .. points" are acquired from a rotating sonar device, and the "lines" are extracted from a user-entered line-segment model of the building. The algorithm assumes that only small corrections are necessary to correct for odometry errors which inherently accumulate, and makes a correction by shifting and rotating the sonar image so that the data points are as close as possible to the lines. A modification of the basic algorithm to accommodate parallel lines was developed as well as an improvement to the basic noise removal algorithm. We found that the matching algorithm was able to determine the location of the robot to within one foot even when required to correct for as many as five feet of simulated odometry error. Finally, the algorithm's complexity was found to be well within the processing power of currently available hardware.

No description available.