Lightweight design and encoderless control of a miniature direct drive linear delta robot

This paper presents the design, integration and experimental validation of a miniature light-weight delta robot targeted to be used for a variety of applications including the pick-place operations, high speed precise positioning and haptic implementations. The improvements brought by the new design contain; the use of a novel light-weight joint type replacing the conventional and heavy bearing structures and realization of encoderless position measurement algorithm based on hall effect sensor outputs of direct drive linear motors. The description of mechanical, electrical and software based improvements are followed by the derivation of a sliding mode controller to handle tracking of planar closed curves represented by elliptic fourier descriptors (EFDs). The new robot is tested in experiments and the validity of the improvements are verified for practical implementation

DESARROLLODE UN ROBOT DELTA PARALELO TIPO KEOPS CON ESTRUCTURA MODIFICABLE

En el presente artículose exponen el estudio, diseño y desarrollo de un robot paralelo tipo Keops con estructura modificable construido específicamentepara operar en un espacio de trabajo con forma cilíndrica. Se presenta en este artículo las etapas del estudio, diseño y desarrollo de robot, la aplicación de técnicas de algoritmos genéticos para el cálculo óptimo de las dimensiones y laarquitectura de control implementada para regular corriente, velocidad y posiciónen motores BrushlessDC. Se finaliza exponiendo los resultados obtenidos del desarrollo del robot paralelo tipo Keops con estructura modificabl

A SERIAL-PARALLEL HYBRID ROBOT FOR MACHINING OF COMPLEX SURFACES

Ph.DDOCTOR OF PHILOSOPH

Functional Autonomy Techniques for Manipulation in Uncertain Environments

As robotic platforms are put to work in an ever more diverse array of environments, their ability to deploy visuomotor capabilities without supervision is complicated by the potential for unforeseen operating conditions. This is a particular challenge within the domain of manipulation, where significant geometric, semantic, and kinetic understanding across the space of possible manipulands is necessary to allow effective interaction. To facilitate adoption of robotic platforms in such environments, this work investigates the application of functional, or behavior level, autonomy to the task of manipulation in uncertain environments. Three functional autonomy techniques are presented to address subproblems within the domain. The task of reactive selection between a set of actions that incur a probabilistic cost to advance the same goal metric in the presence of an operator action preference is formulated as the Obedient Multi-Armed Bandit (OMAB) problem, under the purview of Reinforcement Learning. A policy for the problem is presented and evaluated against a novel performance metric, disappointment (analogous to prototypical MAB's regret), in comparison to adaptations of existing MAB policies. This is posed for both stationary and non-stationary cost distributions, within the context of two example planetary exploration applications of multi-modal mobility, and surface excavation. Second, a computational model that derives semantic meaning from the outcome of manipulation tasks is developed, which leverages physics simulation and clustering to learn symbolic failure modes. A deep network extracts visual signatures for each mode that may then guide failure recovery. The model is demonstrated through application to the archetypal manipulation task of placing objects into a container, as well as stacking of cuboids, and evaluated against both synthetic verification sets and real depth images. Third, an approach is presented for visual estimation of the minimum magnitude grasping wrench necessary to extract massive objects from an unstructured pile, subject to a given end effector's grasping limits, that is formulated for each object as a "wrench space stiction manifold". Properties are estimated from segmented RGBD point clouds, and a geometric adjacency graph used to infer incident wrenches upon each object, allowing candidate extraction object/force-vector pairs to be selected from the pile that are likely to be within the system's capability.</p

Augmented Reality Navigation Interfaces Improve Human Performance In End-Effector Controlled Telerobotics

On the International Space Station (ISS) and space shuttles, the National Aeronautics and Space Administration (NASA) has used robotic manipulators extensively to perform payload handling and maintenance tasks. Teleoperating robots require expert skills and optimal performance is crucial to mission completion and crew safety. Degradation in performance is observed when manual control is mediated through remote camera views, resulting in poor end-effector navigation quality and extended task completion times. This thesis explores the application of three-dimensional augmented reality (AR) interfaces specifically designed to improve human performance during end-effector controlled teleoperations. A modular telerobotic test bed was developed for this purpose and several experiments were conducted. In the first experiment, the effect of camera placement on end-effector manipulation performance was evaluated. Results show that increasing misalignment between the displayed end-effector and hand-controller axes (display-control misalignments) increases the time required to process a movement input. Simple AR movement cues were found to mitigate the adverse effects of camera-based teleoperation and made performance invariant to misalignment. Applying these movement cues to payload transport tasks correspondingly demonstrated improvements in free-space navigation quality over conventional end-effector control using multiple cameras. Collision-free teleoperations are also a critical requirement in space. To help the operators guide robots safely, a novel method was evaluated. Navigation plans computed by a planning agent are presented to the operator sequentially through an AR interface. The plans in combination with the interface allow the operator to guide the end-effector through collision-free regions in the remote environment safely. Experimental results show significant benefits in control performance including reduced path deviation and travel distance. Overall, the results show that AR interfaces can improve performance during manual control of remote robots and have tremendous potential in current and future teleoperated space robotic systems; as well as in contemporary military and surgical applications

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Advances in Robot Kinematics : Proceedings of the 15th international conference on Advances in Robot Kinematics

International audienceThe motion of mechanisms, kinematics, is one of the most fundamental aspect of robot design, analysis and control but is also relevant to other scientific domains such as biome- chanics, molecular biology, . . . . The series of books on Advances in Robot Kinematics (ARK) report the latest achievement in this field. ARK has a long history as the first book was published in 1991 and since then new issues have been published every 2 years. Each book is the follow-up of a single-track symposium in which the participants exchange their results and opinions in a meeting that bring together the best of world’s researchers and scientists together with young students. Since 1992 the ARK symposia have come under the patronage of the International Federation for the Promotion of Machine Science-IFToMM.This book is the 13th in the series and is the result of peer-review process intended to select the newest and most original achievements in this field. For the first time the articles of this symposium will be published in a green open-access archive to favor free dissemination of the results. However the book will also be o↵ered as a on-demand printed book.The papers proposed in this book show that robot kinematics is an exciting domain with an immense number of research challenges that go well beyond the field of robotics.The last symposium related with this book was organized by the French National Re- search Institute in Computer Science and Control Theory (INRIA) in Grasse, France

Path and Motion Planning for Autonomous Mobile 3D Printing

Autonomous robotic construction was envisioned as early as the ‘90s, and yet, con- struction sites today look much alike ones half a century ago. Meanwhile, highly automated and efficient fabrication methods like Additive Manufacturing, or 3D Printing, have seen great success in conventional production. However, existing efforts to transfer printing technology to construction applications mainly rely on manufacturing-like machines and fail to utilise the capabilities of modern robotics. This thesis considers using Mobile Manipulator robots to perform large-scale Additive Manufacturing tasks. Comprised of an articulated arm and a mobile base, Mobile Manipulators, are unique in their simultaneous mobility and agility, which enables printing-in-motion, or Mobile 3D Printing. This is a 3D printing modality, where a robot deposits material along larger-than-self trajectories while in motion. Despite profound potential advantages over existing static manufacturing-like large- scale printers, Mobile 3D printing is underexplored. Therefore, this thesis tack- les Mobile 3D printing-specific challenges and proposes path and motion planning methodologies that allow this printing modality to be realised. The work details the development of Task-Consistent Path Planning that solves the problem of find- ing a valid robot-base path needed to print larger-than-self trajectories. A motion planning and control strategy is then proposed, utilising the robot-base paths found to inform an optimisation-based whole-body motion controller. Several Mobile 3D Printing robot prototypes are built throughout this work, and the overall path and motion planning strategy proposed is holistically evaluated in a series of large-scale 3D printing experiments

Type Synthesis and Performance Optimization of Parallel Manipulators

Parallel robots have been widely employed in industrial applications. There are still some challenging topics in the fundamental research, e.g., the primary problem mobility analysis has not been solved for about 150 years. A universal mobility equation for all kinds of parallel architectures has not been found. Another issue lies on the performance measurements for parallel manipulators. There are plenty of kinematic and dynamic performance indices. However, the various ranges and scales of these indicators make the optimal design considering multiple indices complicated. It is essential to search for a unified approach to normalize performance indicators. More dynamic performance measurement indicators should be raised to explore the dynamic features and complete the theory for parallel mechanisms. In this research, an improved mobility equation is designed to reveal the degrees of freedom for a special class of parallel robots. A novel methodology called the kinematic joint matrix is proposed. It possesses the mapping relations with parallel manipulators. A series of 2-6 degrees of freedom parallel architectures is denoted by the kinematic joint matrix. The theory of screw is employed to check the feasibility from several kinds of parallel structures. A special block diagram is introduced to distinguish various kinematic joint matrices. Since this family of parallel robots contains various motion characteristics, four parallel robots with distinct features are selected. Based on the kinematic models, three categories of singularities are explored. The operational and reachable workspaces of the pure-translational parallel robots are searched and the parametric analyses are reported. The linkage’s impacts for the reachable workspace of the mixed-motion parallel architectures are investigated. The novel performance level index is designed to unify the positive performance index and demonstrated the performance rank for any pose (position and orientation). The dexterity index is utilized as an example to verify the characteristics of the level index. The distributions and parametric analyses of two novel mass-related performances are studied. The dimension synthesis of a selected planar parallel robot is presented based on the non-dominated genetic algorithm II. The experiment results testify the correctness of the mobility and kinematic mathematical models of this mechanism

Assistive control for non-contact machining of random shaped contours

Recent achievements in robotics and automation technology has opened the door towards different machining methodologies based on material removal. Considering the non force feedback nature of non-contact machining methods, careful attention on motion control design is a primary requirement for successful achievement of precise cutting both in machining and in surgery processes. This thesis is concerned with the design of pre-processing methods and motion control techniques to provide both automated and human-assistive non-contact machining of random and complex shaped contours. In that sense, the first part of the thesis focuses on extraction of contours and generation of reference trajectories or constraints for the machining system. Based on generated trajectories, two different control schemes are utilized for high precision automated machining. In the first scheme, preview control is adopted for enhancing the tracking performance. In the second scheme, control action is generated based on direct computation of contouring error in the operational space by introducing a new coordinate frame moving with the reference contour. Further, non-contact machining is extended for realization in a master/slave telerobotic framework to enable manual remote cutting by a human operator. With the proposed approach, the human operator (i.e. a surgeon) is limited to conduct motion within a desired virtual constraint and is equipped with the ability of adjusting the cutting depth over a that contour providing advantage for laser surgery applications. The proposed framework is experimentally tested and results of the experiments prove the applicability of proposed motion control schemes and show the validity of contributions made in the context of thesis