In this thesis, a kinematic model for controlling an Omni-directional 3 DOF DDR with an external navigation point is presented. Two different dynamic models for investigating the resulting torque on the three active motors on the robot are also developed and validated. The focus of the thesis is on the design of kinematic and dynamic models in an ideal environment and the kinematic model in a high fidelity environment. The kinematic model uses inverse kinematics to translate the controlling motion reference from the external navigation point to the three active motors on the DDR. The thesis also includes a comparison of the two different dynamic models based on Kane's method and Newton's second law of motion, respectively. The models presented in this thesis could aid autonomous robots with attached payloads such as hospital beds move with the centre of geometry as a focal point and thereby perform movements. The results show that such a kinematic model for controlling the specific robot is shown to be feasible in an ideal environment. However, due to PID controllers for the active wheel motors not being exact enough, the model in the high fidelity environment does not perform correctly in all cases. Furthermore, the dynamic model results provide an understanding of the difference of torque dependant on the distance to the navigation point.

In this thesis, a suitable mechanical design of a di erential drive robot equipped with a force sensingturntable is presented for a logistical robot application in a hospital environment. The focus of thisthesis will be on the mechanical design of the robot, the force sensor, electronics for the force sensorand the development of a dynamic model. The dynamic model takes advantage of the payloadinformation from the force sensor in order to manoeuvre the robot optimally without it falling over.A complete method of measuring and locating a load on top of the table is successfully tested. Asimulation of the robot using the dynamic model and information about external forces from theforce sensor is created, applied and veri ed towards a set of tests with expected reactions fromthe model. The robot platform was built mechanically but not fully assembled with electronics,power sources, etc. The simulations and tests performed upon the robot platform indicates that itful ls the requirements set up in this paper. The dynamic model has not been tested on a physicalplatform but it behaves exactly like expected in the simulations hence tuning is the only expectedobstruction before it can be applied to the real-life platform. The force sensor works as intendedand can accurately measure payload mass by 0:2kg and lever lengths by 0:1m to the payload.The accuracy of the angle approximation relies on the magnitude of the radial torque signal, hencea threshold of the acceptable radial torque magnitude must be determined in order to in turnde ne the worst acceptable radial angle accuracy.

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Nesta dissertação é realizado um estudo comparativo entre controladores robustos projetados para sistemas lineares em espaço de estado sujeitos a incertezas paramétricas. O objetivo é resolver problemas de acompanhamento de trajetória de robôs. O estudo é realizado em um robô móvel com tração diferencial e em um quadricóptero. Para tal, é aplicado um Regulador Linear Quadrático Robusto no qual engloba em uma estrutura unificada todos os parâmetros de incerteza de entrada e saída de maneira recursiva, útil em aplicações em tempo real. A fim de demonstrar a eficiência do Regulador Robusto, resultados de simulações e de experimentos são empregados comparando-o com controle Η∞ não linear via teoria dos jogos e com um controle Proporcional-Derivativo mais torque calculado. / This work provides a comparative study between robust controllers for linear statespace systems subject to parametric uncertainties to solve trajectory tracking problems. The study is developed in a mobile robot with differential traction and in a quadricopter. A Robust Linear Quadratic Regulator is applied, which encompasses in a unified framework all input and output uncertain parameters, useful in online applications. In order to show the effectiveness of the robust regulator, simulations and experiments results allow the comparison with nonlinear Η∞ control via game theory and with a Proportional- Derivative control plus computed torque.

abstract: Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design and control objectives for ground vehicles. One central objective is formation of multi-robot systems, particularly, longitudinal control of platoon of ground vehicle. In this thesis, the author use low-cost ground robot platform shows that with leader information, the platoon controller can have better performance than one without it. Based on measurement from multiple vehicles, motor-wheel system dynamic model considering gearbox transmission has been developed. Noticing the difference between on ground vehicle behavior and off-ground vehicle behavior, on ground vehicle-motor model considering friction and battery internal resistance has been put forward and experimentally validated by multiple same type of vehicles. Then simplified longitudinal platoon model based on on-ground test were used as basis for platoon controller design. Hardware and software has been updated to facilitate the goal of control a platoon of ground vehicles. Based on previous work of Lin on low-cost differential-drive (DD) RC vehicles called Thunder Tumbler, new robot platform named Enhanced Thunder Tumbler (ETT 2) has been developed with following improvement: (1) optical wheel-encoder which has 2.5 times higher resolution than magnetic based one, (2) BNO055 IMU can read out orientation directly that LSM9DS0 IMU could not, (3) TL-WN722N Wifi USB Adapter with external antenna which can support more stable communication compared to Edimax adapter, (4) duplex serial communication between Pi and Arduino than single direction communication from Pi to Arduino, (5) inter-vehicle communication based on UDP protocol. All demonstrations presented using ETT vehicles. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) longitudinal platoon control based on local information (ultrasonic sensor) without inter-vehicle communication, (3) longitudinal platoon control based on local information (ultrasonic sensor) and leader information (speed). Hardware data/video is compared with, and corroborated by, model-based simulations. Platoon simulation and hardware data reveals that with necessary information from platoon leader, the control effort will be reduced and space deviation be diminished among propagation along the fleet of vehicles. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

Nesta dissertação é realizado um estudo comparativo entre controladores robustos projetados para sistemas lineares em espaço de estado sujeitos a incertezas paramétricas. O objetivo é resolver problemas de acompanhamento de trajetória de robôs. O estudo é realizado em um robô móvel com tração diferencial e em um quadricóptero. Para tal, é aplicado um Regulador Linear Quadrático Robusto no qual engloba em uma estrutura unificada todos os parâmetros de incerteza de entrada e saída de maneira recursiva, útil em aplicações em tempo real. A fim de demonstrar a eficiência do Regulador Robusto, resultados de simulações e de experimentos são empregados comparando-o com controle Η∞ não linear via teoria dos jogos e com um controle Proporcional-Derivativo mais torque calculado. / This work provides a comparative study between robust controllers for linear statespace systems subject to parametric uncertainties to solve trajectory tracking problems. The study is developed in a mobile robot with differential traction and in a quadricopter. A Robust Linear Quadratic Regulator is applied, which encompasses in a unified framework all input and output uncertain parameters, useful in online applications. In order to show the effectiveness of the robust regulator, simulations and experiments results allow the comparison with nonlinear Η∞ control via game theory and with a Proportional- Derivative control plus computed torque.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.

abstract: Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 􀀀175 but offers the capability of commercially available vehicles costing over 􀀀500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws. All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Nesta dissertação é apresentada a comparação do desempenho de três estimadores - o Filtro de Kalman Estendido, o Filtro de Kalman Unscented e o Filtro de Partículas - aplicados para estimar a postura de um robô diferencial. Uma câmera foi fixa no teto para cobrir todo o campo operacional do robô durante os experimentos, a fim de extrair o mapa e gerar o ground truth. Isso permitiu realizar uma análise do erro de forma precisa a cada instante de tempo. O desempenho de cada um dos estimadores foi avaliado sistematicamente e numericamente para duas trajetórias. Os resultados desse primeiro experimento demonstram que os filtros proporcionam grandes melhorias em relação à odometria e que o modelo dos sensores é crítico para obter esse desempenho. O Filtro de Partículas mostrou um desempenho melhor em relação aos demais nos dois percursos. No entanto, seu elevado custo computacional dificulta sua implementação em uma aplicação de tempo real. O Filtro de Kalman Unscented, por sua vez, mostrou um desempenho semelhante ao Filtro de Kalman Estendido durante a primeira trajetória. Porém, na segunda trajetória, a qual possui uma quantidade maior de curvas, o Filtro de Kalman Unscented mostrou uma melhora significativa em relação ao Filtro de Kalman Estendido. Foi realizado um segundo experimento, em que o robô planeja e executa duas trajetórias. Os resultados obtidos mostraram que o robô consegue chegar a um determinado local com uma precisão da mesma ordem de grandeza do que a obtida durante a estimação de estados do robô. / In this dissertation, the performance of three nonlinear-model based estimators - the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter - applied to pose estimation of a differential drive robot is compared. A camera was placed above the operating field of the robot to record the experiments in order to extract the map and generate the ground truth so the evaluation of the error can be done at each time step with high accuracy. The performance of each estimator is assessed systematically and numerically for two robot trajectories. The first experimental results showed that all estimators provide large improvements with respect to odometry and that the sensor modeling is critical for their performance. The particle filter showed a better performance than the others on both experiments, however, its high computational cost makes it difficult to implement in a real-time application. The Unscented Kalman Filter showed a similar performance to the Extended Kalman Filter during the first trajectory. However, during the second one (a curvier path) the Unscented Kalman Filter showed a significant improvement over the Extended Kalman Filter. A second experiment was carried out where the robot plans and executes a trajectory. The results showed the robot can reach a predefined location with an accuracy of the same order of magnitude as the obtained during the robot pose estimation.