Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from `scratch,' making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge is incorporated into the LWPR framework by initializing the local linear models with a first order approximation of the prior information. Second, prior knowledge is incorporated into the mean function of Sparse Online Gaussian Processes (SOGP) and Sparse Pseudo-input Gaussian Processes (SPGP), and a modified version of the algorithm is proposed to allow for online, incremental updates. It is shown that the proposed approaches allow the system to operate well even without any initial training data, and further performance improvement can be achieved with additional online training. Furthermore, it is also shown that even partial knowledge of the system dynamics, for example, only the gravity loading vector, can be used effectively to initialize the learning.

In this thesis, a full design, development and application of an autonomous robotic automation system using vision feedback is performed. To realize this system, a cylindrical manipulator configuration is implemented, using a personal computer (PC) based PID controller from National Instruments. Full autonomous control will be achieved via a programmable human machine interface (HMI) developed on a PC using Borland C++ Builder. The vision feedback position control is accomplished using an ordinary "off-the-shelf" web camera. The manuscript is organized as follows; After Chapter 1, an introduction to automation history and its role in the manufacturing industry, Chapter 2 discusses and outlines the development of the robotic kinematics and dynamics of the system. A control strategy is also developed and simulated in this chapter. Chapter 3 discusses color image processing and shows the development of the algorithm used for the vision feedback position control. Chapter 4 outlines the system development, which includes the hardware and software. Chapter 5 concludes with a summary, and improvement section. The process used as a basis for the design and development of this thesis of this thesis topic was constructed from a manual capacitor orientation check test station. A more detailed definition and objective is presented in the introduction.

This thesis proposes a general and unified methodological framework suitable for studying the locomotion of a wide range of robots, especially bio-inspired. The objective of this thesis is twofold. First, it contributes to the classification of locomotion robots by adopting the mathematical tools developed by the American school of geometric mechanics.Secondly, by taking advantage of the recursive nature of the Newton-Euler formulation, it proposes numerous efficient tools in the form of computational algorithms capable of solving the external direct dynamics and the internal inverse dynamics of any locomotion robot considered as a mobile multi-body system. These generic tools can help the engineers or researchers in the design, control and motion planning of manipulators as well as locomotion robots with a large number of internal degrees of freedom. The efficient algorithms are proposed for discrete and continuous robots. These methodological tools are applied to numerous illustrative examples taken from the bio-inspired robotics such as snake-like robots, caterpillars, and others like snake-board, etc.

No description available.

This thesis introduces a new measure of balance for bipedal robotics called the foot placement estimator (FPE). To develop this measure, stability first is defined for a simple biped. A proof of the stability of a simple biped in a controls sense is shown to exist using classical methods for nonlinear systems. With the addition of a contact model, an analytical solution is provided to define the bounds of the region of stability. This provides the basis for the FPE which estimates where the biped must step in order to be stable. By using the FPE in combination with a state machine, complete gait cycles are created without any precalculated trajectories. This includes gait initiation and termination. The bipedal model is then advanced to include more realistic mechanical and environmental models and the FPE approach is verified in a dynamic simulation. From these results, a 5-link, point-foot robot is designed and constructed to provide the final validation that the FPE can be used to provide closed-loop gait control. In addition, this approach is shown to demonstrate significant robustness to external disturbances. Finally, the FPE is shown in experimental results to be an unprecedented estimate of where humans place their feet for walking and jumping, and for stepping in response to an external disturbance.

This thesis introduces a new measure of balance for bipedal robotics called the foot placement estimator (FPE). To develop this measure, stability first is defined for a simple biped. A proof of the stability of a simple biped in a controls sense is shown to exist using classical methods for nonlinear systems. With the addition of a contact model, an analytical solution is provided to define the bounds of the region of stability. This provides the basis for the FPE which estimates where the biped must step in order to be stable. By using the FPE in combination with a state machine, complete gait cycles are created without any precalculated trajectories. This includes gait initiation and termination. The bipedal model is then advanced to include more realistic mechanical and environmental models and the FPE approach is verified in a dynamic simulation. From these results, a 5-link, point-foot robot is designed and constructed to provide the final validation that the FPE can be used to provide closed-loop gait control. In addition, this approach is shown to demonstrate significant robustness to external disturbances. Finally, the FPE is shown in experimental results to be an unprecedented estimate of where humans place their feet for walking and jumping, and for stepping in response to an external disturbance.

No description available.

Cette thèse propose un cadre méthodologique général et unifié adapté à l’étude de la locomotion d'une large gamme de robots, en particulier bio-inspirés. L'objectif de cette thèse est double. Tout d'abord, elle contribue à la classification des robots locomoteurs en adoptant les outils mathématiques mis en place par l'école américaine de mécanique géométrique. Deuxièmement,en profitant de la nature récursive de la formulation de Newton-Euler, elle propose de nouveaux outils efficaces sous la forme d'algorithmes aptes à résoudre les dynamiques externe directe et interne inverse de tout robot locomoteur approximable par un système multicorps mobile. Ces outils génériques peuvent aider l’ingénieur ou le chercheur dans la conception, la commande, la planification de mouvement des robots locomoteurs ou manipulateurs comprenant un grand nombre de degrés de liberté internes. Des algorithmes effectifs sont proposés pour les robots discrets ainsi que continus. Ces outils méthodologiques sont appliqués à de nombreux exemples illustratifs empruntés à la robotique bio-inspirée tels les robots serpents, chenilles et autres snake-board… / This thesis proposes a general and unified methodological framework suitable for studying the locomotion of a wide range of robots, especially bio-inspired. The objective of this thesis is twofold. First, it contributes to the classification of locomotion robots by adopting the mathematical tools developed by the American school of geometric mechanics.Secondly, by taking advantage of the recursive nature of the Newton-Euler formulation, it proposes numerous efficient tools in the form of computational algorithms capable of solving the external direct dynamics and the internal inverse dynamics of any locomotion robot considered as a mobile multi-body system. These generic tools can help the engineers or researchers in the design, control and motion planning of manipulators as well as locomotion robots with a large number of internal degrees of freedom. The efficient algorithms are proposed for discrete and continuous robots. These methodological tools are applied to numerous illustrative examples taken from the bio-inspired robotics such as snake-like robots, caterpillars, and others like snake-board, etc.