Study of Motion Control of A Flexible Link

20th century has witnessed massive upsurge in the use of manipulators in several industries especially in space, defense, and medical industries. Among the types of manipulators used, single link manipulators are the most widely used. A single link robotic manipulator is nothing but a link controlled by an actuator to carry out a particular function such as placing a payload from point A to point B. For low power requirements single link manipulators are made up of light weight materials which require flexibility considerations.Flexibility makes the dynamics of the link heavily non-linear which induces vibrations and overshoot. In this project initially the dynamic model of rigid flexible manipulator is explained, then the state space model of the manipulator system is incorporated into MATLAB. The link flexibility is studied by a single beam FEmodel, where expressions for kinetic and potential energyare employed to derive the torqueequation.The 3 flexible link equations are coupled in terms of 3 variables, θ, Ø and v. The tip angle is finally given aslvfor flexible case whereas for the rigid manipulator the tip angle is same as the hub angle θ. Thereforeaccurate computation of v is very important. The joint flexibility is excluded from analysis.Several comparisons were made between the rigid and flexible link for torque requirement. The relation between the trajectory and hub angle is also plotted in a graph.Finally a PD controller taking the errors and its derivative is designed based on the rigid link dynamics

Optimizing task placement in robotic cells

The primary objective of this dissertation is to develop novel and practical techniques for optimal task placement in robotic cells. To this end, it is shown how task placement affect the efficiency of the cell, whether the task is automated fiber placement to create composite materials, gluing or inspection. Here, efficiency of the cell is defined by either cycle time of the production or distance to singularity, having collision avoidance as a constraint. Task placement, even for one robotic arm, is an under-constrained problem in nature. This issue drastically grows in case of redundant robotic cells. Actuator redundancy in robotic cells is added by either a positioner or another manipulator. This work is focused on taking advantage of redundancy in robotic cells and optimizing it for better performance. One of the main challenges here is to identify the number of independent placement parameters. Therefore, we ignore ineffective variables and only focus on minimum number of parameters possible. Hence, faster optimization process and more precise results are obtained. Another challenge is in motion planning of redundant cells. Because there can be infinite solutions for such cells, there is room for optimization. In this work, we propose methods to fix the optimal placement of the task and, furthermore, assign the optimal motion planning to all manipulators in the cell, simultaneously. A novel method is proposed to identify the number of independent parameters and applied to a gluing path for a coordinated redundant robotic workcell. The workcell consists of a generic six-DOF serial manipulator and a one-DOF redundancy provider (RP). Two cases of RPs are investigated, namely a rotary table and a linear guide. An innovative method using swept volume is proposed for determining the number of independent parameters for both cases under study. The outcome of this study is an intuitive method to identify the number of independent parameters in redundant cells. The results are compared between using all initial parameters, as contrary to only the independent ones. It is proven that the proposed method improves the optimization efficiency by 32%. Moreover, the performance of the rotary table is compared to the linear guide, for a specific gluing application. Optimization methods in this work are based on Particle Swarm Optimization (PSO). A workcell consisting of a six degrees of freedom (DOF) serial manipulator, a six-DOF parallel manipulator and a rotary table mounted on the parallel manipulator is studies for automated fiber placement task. The solution to motion planning is obtained considering the singularities of the serial manipulator and the workspace boundaries of all manipulators. The algorithm to obtain the optimum path placement is explained through a simple example and the results for a helix path with nearly 2,700 points around the workpiece is represented. The results for motion planning are represented where distance to singularity is maximized, collision avoidance and workspace boundaries are respected. The result is obtained after 10 iterations with 20 particles. This outcome of this study is a reliable and easy to apply motion planning algorithm to be used in redundant cells. Another challenge in this work is combinatorial task placement that arises in robotic inspection cells. The goal is to improve the efficiency of a turbine blade inspection cell through optimizing the placement of the camera and optimizing the sequence of the images. The workcell contains a six-DOF serial manipulator that is holding the blade and shows it to the camera from different angles, whereas the camera takes inspection images. The problem at hand consists of a six-DOF continuous optimization for camera placement and discrete combinatorial optimization of sequence of images (end-effector poses). A novel combined approach is introduced, called Blind Dynamic Particle Swarm Optimization (BD-PSO), to simultaneously obtain the optimal design for both domains. Our objective is to minimize the cycle time, while avoiding any collisions in the workcell during the inspection operation. Even though PSO is vastly used in engineering problems, novelty of the proposed combinatorial optimization method is in its ability to be used efficiently in the traveling salesman problems where the distances between cities are unknown (blind) and the distances are subject to change (dynamic). This highly unpredictable domain is the case of the inspection cell where the cycle time between images will change for different camera placements. The cycle time is calculated based on weighted joint travel time of the robot. All the eight configurations of the robot are taken into the consideration, therefore, robot’s configuration is optimized in the final result as well. The outcome of this study is an innovative hybrid algorithm to simultaneously solve combinatorial and continues problems. Results show fast convergence and reliable motions. The test of benchmarks selected from TSPLIB shows that the results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness than those obtained by other methods. The best placement of camera and best image sequence (for 8 images) is obtained after 11 iterations using 30 particles. In general, the main results of this thesis are three algorithms: an algorithm to obtain minimum number of placement parameters in redundant robotic workcells; an algorithm for motion planning of highly redundant cells; and an algorithm to optimize camera placement and simultaneously obtain the optimal image sequence in an inspection cell

Optimisation of docking locations for remotely operated vehicles.

This thesis describes work aimed at developing practical methods for determining the best docking locations for an underwater remotely operated vehicle (ROV) when inspecting an offshore platform. ROVs are used extensively in the offshore oil and gas industry to conduct a large variety of intervention tasks such as visual inspection, operational monitoring, equipment installation and operation, debris recovery, and so on. However, they have found only limited use in the more difficult tasks such as the detailed inspection of complex weld geometries. These complex welds are, however, found extensively in the construction of the majority of offshore structures and platforms ('oil rigs'). Furthermore, there is a safety requirement to have them inspected regularly since failure of these welds can potentially lead to catastrophic failure of the structures, the majority of which are manned. A number of specialist ROV systems have been developed that are able to attach onto platform structures and use their manipulators to conduct inspection. However, due to the short reach of the manipulators and the complex geometry of the welds (often encumbered with protruding pipes and other fittings) the success of any inspection is crucially dependent on a good initial choice of ROV docking position. This thesis will describe the problems and current manual planning methods, and then detail the development of two new methods for automated optimisation of docking positions - firstly using neural networks, and secondly using more conventional numerical processing. This thesis will also review related work in the field, such as the development of neural networks and their applications in the general offshore environment and in the control of ROVs and robot manipulator arms, and other approaches to ROV docking. It will further describe the use of the system developed here for planning docking positions on example commercial ROV inspection work programmes

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Kraftsensorlose Manipulator Kraftsteuerung zur Abtastung unbekannter, harter Oberflächen

Die vorliegende Arbeit zeigt ein Verfahren zur kraftgesteuerten Kontaktierung unbekannter harter Freiformflächen mit einem Standard–6DOF-Industriemanipulator (z.B. Manutec R2). Die bisher entwickelten Verfahren auf dem Gebiet der Manipulatorkraftregelung waren auf teure, fragile, mehrdimensionale Kraft-/Momentensensoren am Manipulator-Endeffektor angewiesen, die bei dem in dieser Arbeit entwickelten Ansatz der sensorlosen Kraft-/Geschwindigkeitsregelung überflüssig werden. Die Einstellung der gewünschten Kontaktkraft zu der unbekannten Umgebung erfolgt ausschließlich über eine robuste, beobachtergestützte Regelung der Motorströme der Gelenkantriebe. In freien Bewegungsphasen garantierte eine kaskadierte Kraft-/Geschwindigkeitsregelung vordefinierte Heranfahrgeschwindigkeiten an die unbekannte Kontaktoberfläche. Hierdurch eröffnen sich vollkommen neue Einsatzszenarien für die kraftkontrollierte Kontaktierung und Bearbeitung unbekannter Oberflächen oder Werkstücke beliebiger Härte und Steifigkeit

Robotic joint-motion optimization of functionally-redundant tasks for joint-limits and singularity avoidance

La méthodologie de décomposition du torseur de vitesse (TWA) et évitement des limites articulaires -- Évitement des limites articulaires et singularités -- auto-adaptation des poids en TWA -- Adaptation dynamique des pondération en TWA -- Background and basic terminology -- Problem formulation -- Research objective -- Literature review -- Level of kinematic analysis -- Differential kinematics and redundancy -- Local optimization algorithms -- Global optimization algorithms -- Redundancy-resolution in intelligent control -- Functional redundancy-resolution -- Twist decomposition approach and joint-limits avoidance -- Kinematic inversion of functionally-redundant manipulators -- Puma 500 -- Fanuc M16iB -- Fanuc 710c50 -- General task projectors -- Joint-limits and singularity avoidance in TWA -- Performance criteria -- Numerical examples -- Self-adaptation of weights in TWA -- Joint-limits and singularity avoidances -- Weights self-adaptation system -- Dynamic-adaptation of weights in TWA -- Weights dynamic-adaptation system

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Multimodal series elastic actuator for human-machine interaction with applications in robot-aided rehabilitation

Series elastic actuators (SEAs) are becoming an elemental building block in collaborative robotic systems. They introduce an elastic element between the mechanical drive and the end-effector, making otherwise rigid structures compliant when in contact with humans. Topologically, SEAs are more amenable to accurate force control than classical actuation techniques, as the elastic element may be used to provide a direct force estimate. The compliant nature of SEAs provides the potential to be applied in robot-aided rehabilitation. This thesis proposes the design of a novel SEA to be used in robot-aided musculoskeletal rehabilitation. An active disturbance rejection controller is derived and experimentally validated and multiobjective optimization is executed to tune the controller for best performance in human-machine interaction. This thesis also evaluates the constrained workspaces for individuals experiencing upper-limb musculoskeletal disorders. This evaluation can be used as a tool to determine the kinematic structure of devices centred around the novel SEA

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care