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

Kinematic Analysis and Trajectory Planning of the Orthoglide 5-axis

The subject of this paper is about the kinematic analysis and the trajectory planning of the Orthoglide 5-axis. The Orthoglide 5-axis a five degrees of freedom parallel kinematic machine developed at IRCCyN and is made up of a hybrid architecture, namely, a three degrees of freedom translational parallel manip-ulator mounted in series with a two degrees of freedom parallel spherical wrist. The simpler the kinematic modeling of the Or-thoglide 5-axis, the higher the maximum frequency of its control loop. Indeed, the control loop of a parallel kinematic machine should be computed with a high frequency, i.e., higher than 1.5 MHz, in order the manipulator to be able to reach high speed motions with a good accuracy. Accordingly, the direct and inverse kinematic models of the Orthoglide 5-axis, its inverse kine-matic Jacobian matrix and the first derivative of the latter with respect to time are expressed in this paper. It appears that the kinematic model of the manipulator under study can be written in a quadratic form due to the hybrid architecture of the Orthoglide 5-axis. As illustrative examples, the profiles of the actuated joint angles (lengths), velocities and accelerations that are used in the control loop of the robot are traced for two test trajectories.Comment: Appears in International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference, Aug 2015, Boston, United States. 201

Online Learning of the Inverse Dynamics with Parallel Drifting Gaussian Processes: Implementation of an Approach for Feedforward Control of a Parallel Kinematic Industrial Robot

The present paper deals with an online approach to learn the inverse dynamics of any robot. This is realized by the use of Gaussian Processes drifting parallel along the system data. An extension by a database enables the efficient use of data points from the past. The central component of this work is the implementation of such a method in a controller in order to achieve the actual goal: the feedforward control of an industrial robot by means of machine learning. This is done by splitting the procedure into two threads running parallel so that the prediction is decoupled from the computing-intensive training of the models. Experiments show that the method reduces the tracking errors more clearly than an elaborately identified rigid body model including friction. For a defined trajectory, the squared areas of the tracking errors of all axes are reduced by more than 54% compared to motion without pre-control. In addition, a highly dynamic pick-and-place experiment is used to investigate the possible changes in system dynamics. Compared to an offline trained model, the approximation error of the proposed online approach is smaller for the remaining time of the experiment after an initial phase. Furthermore, this error is smaller throughout the experiment for online learning with parallel drifting Gaussian Processes than when using a single one

Visual enhancements in pick-and-place tasks: Human operators controlling a simulated cylindrical manipulator

A teleoperation simulator was constructed with vector display system, joysticks, and a simulated cylindrical manipulator, in order to quantitatively evaluate various display conditions. The first of two experiments conducted investigated the effects of perspective parameter variations on human operators' pick-and-place performance, using a monoscopic perspective display. The second experiment involved visual enhancements of the monoscopic perspective display, by adding a grid and reference lines, by comparison with visual enhancements of a stereoscopic display; results indicate that stereoscopy generally permits superior pick-and-place performance, but that monoscopy nevertheless allows equivalent performance when defined with appropriate perspective parameter values and adequate visual enhancements

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage: https://tossingbot.cs.princeton.ed

Parallel manipulators: practical applications and kinematic design criteria. Towards the modular reconfigurable robots

Post-PrintModern robotic manipulators play an essential role in industry, developing several tasks in an easy way, enhancing the accuracy of the final product and reducing the executing time. Also they can be found in other fields as aerospace industry, several medical applications, gaming industry, and so on. In particular, the parallel manipulators have acquired a great relevance in the last years. Indeed, many research activities and projects deal with the study and develop-ment of this type of robots. Nevertheless, usually, a bilateral communication between industry and research does not exist, even among the different existing research areas. This causes a lack of knowledge regarding works that have been carried out, the ones that are under devel-opment and the possible future investigations. Hence, once a specific field of knowledge has acquired a certain level of maturity, it is convenient to reflect its current state of the art. In this sense, the authors of this paper present a review of the different fields in which parallel ma-nipulators have a significant participation, and also the most active research topics in the anal-ysis and design of these robots. Besides, several contributions of the authors to this field are cited.The authors wish to acknowledge the financial support received from the Spanish Government through the "Ministerio de Economía y Competitividad" (Project DPI2015-67626-P (MINECO/FEDER, UE)), the financial support from the Uni-versity of the Basque Country (UPV/EHU) under the program UFI 11/29 and the support to the research group, through the project with ref. IT949-16, given by the "Departamento de Educación, Política Lingüística y Cultura" of the Regional Government of the Basque Country

Parallel Manipulators

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications