Aprendizaje de la cinemática en robots redundantes utilizando mapas de bézier

AbstractModel the behavior of redundant robot manipulators is highly complex, which makes difficult inverse kinematics calculus and so on its positioning, to present a solution for this issue we use a very accurate technique named kinematics Bezier maps which learn positioning the end effectors and starting from this we prove some methods of approximation and minimization to solve a specific configurations for each position on Cartesian space. This training does a tool-eye coordination learning from samples of coordinate system referenced to a fixed camera, simultaneously introduces a simplified method flearning to position and orient the end effectors of the robot from position-based trainingEn este trabajo se plantea como novedad un aprendizaje de la cinemática directa empleando mapas de Bézier, técnica que proporciona exactitud del posicionamiento en robots manipuladores, todo ello es debido a que en estos tipos de robots es complejo modelar su comportamiento dificultando con ello el cálculo de la cinemática inversa y por tanto su posicionamiento. A partir del uso de esta técnica se pueden aplicar diversos métodos de aproximación y minimización de funciones que permitan obtener una configuración específica para cada posición en el espacio cartesiano. Dicho entrenamiento logra una coordinación ojo-herramienta, aprendiendo a partir de muestras referenciadas al sistema coordenado de una cámara fija; igualmente se introduce un método de simplificación en el aprendizaje para posicionar y orientar el efector final del robot a partir del entrenamiento basado en posiciones. AbstractModel the behavior of redundant robot manipulators is highly complex, which makes difficult inverse kinematics calculus and so on its positioning, to present a solution for this issue we use a very accurate technique named kinematics Bezier maps which learn positioning the end effectors and starting from this we prove some methods of approximation and minimization to solve a specific configurations for each position on Cartesian space. This training does a tool-eye coordination learning from samples of coordinate system referenced to a fixed camera, simultaneously introduces a simplified method flearning to position and orient the end effectors of the robot from position-based trainin

Controlling Locomotion of a Robotic Leg

Dr. Xing and Professor Refvem are working to research and develop a quadruped robot that is capable of basic movements including walking, running, and jumping. As senior project group F-11, we are joining a team of engineers to assist in the development of the quadruped. Our team was tasked with creating a mathematical model, designing a control method, and implementing that control method for the quadruped\u27s legs in Simulink. This will allow both current and future students to understand the response of the system and provide a building point for future researchers to create working quadrupedal robots. This report documents our research and cumulative work to reach our goals. The report highlights our final design for the controller loop, our implementation process for each controller component, and our design verification tests to justify our work

Review of deep learning methods in robotic grasp detection

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed

A neurodynamic optimization approach to constrained pseudoconvex optimization.

Guo, Zhishan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 71-82).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement i --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Constrained Pseudoconvex Optimization --- p.1Chapter 1.2 --- Recurrent Neural Networks --- p.4Chapter 1.3 --- Thesis Organization --- p.7Chapter 2 --- Literature Review --- p.8Chapter 2.1 --- Pseudo convex Optimization --- p.8Chapter 2.2 --- Recurrent Neural Networks --- p.10Chapter 3 --- Model Description and Convergence Analysis --- p.17Chapter 3.1 --- Model Descriptions --- p.18Chapter 3.2 --- Global Convergence --- p.20Chapter 4 --- Numerical Examples --- p.27Chapter 4.1 --- Gaussian Optimization --- p.28Chapter 4.2 --- Quadratic Fractional Programming --- p.36Chapter 4.3 --- Nonlinear Convex Programming --- p.39Chapter 5 --- Real-time Data Reconciliation --- p.42Chapter 5.1 --- Introduction --- p.42Chapter 5.2 --- Theoretical Analysis and Performance Measurement --- p.44Chapter 5.3 --- Examples --- p.45Chapter 6 --- Real-time Portfolio Optimization --- p.53Chapter 6.1 --- Introduction --- p.53Chapter 6.2 --- Model Description --- p.54Chapter 6.3 --- Theoretical Analysis --- p.56Chapter 6.4 --- Illustrative Examples --- p.58Chapter 7 --- Conclusions and Future Works --- p.67Chapter 7.1 --- Concluding Remarks --- p.67Chapter 7.2 --- Future Works --- p.68Chapter A --- Publication List --- p.69Bibliography --- p.7

Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

A study of a novel modular variable geometry frame arranged as a robotic surface

The novel concept of a variable geometry frame is introduced and explored through a three-dimensional robotic surface which is devised and implemented using triangular modules. The link design is optimized using surplus motor dimensions as firm constraints, and round numbers for further arbitrary constraints. Each module is connected by a passive six-bar mechanism that mimics the constraints of a spherical joint at each triangle intersection. A three dimensional inkjet printer is used to create a six-module prototype designed around surplus stepper motors powered by an old computer power supply as a proof-of-concept example. The finite element method is applied to the static and dynamic loading of this device using linear three dimensional (6 degrees of freedom per node) beam elements to calculate the cartesian displacement and force and the angular displacement and torque at each joint. In this way, the traditional methods of finding joint forces and torques are completely bypassed. An efficient algorithm is developed to linearly combine local stiffness matrices into a full structural stiffness matrix for the easy application of loads. This is then decomposed back into the local matrices to easily obtain joint variables used in the design and open-loop control of the surface. Arbitrary equation driven surfaces are approximated ensuring that they are within the joints limits. Moving shapes are then calculated by considering the initial position of the surface, the desired position of the surface, and intermediate shapes at discrete times along the desired path. There are no sensors on the prototype, but feedback models and state estimators are developed for future use. These models include shape sampling methods derived from existing meshing algorithms, trajectory planning using sinusoidal acceleration profiles, spline-based path approximation to allow lower curvature paths able to be traversed more quickly and/or able to be travelled with a constant velocity and optimized by iteratively calculating actuator saturation with no discontinuities, and the optimal tracking of a desired path (modeled with a time-varying ricatti equation)