Paralleelmehhanismide kinetostaatiliste jõudlusindeksite uuring ning võrdlus

Nii kaua, kui on kasutusel olnud robotid, on käinud teadusuuringud nende kasutamiseks ning töö optimeerimiseks meie igapäevases elus. Samal ajal, kui meie teadmised robotite teemal on suuresti arenenud, on kasvanud ka vastavate struktuuride keerukus. Seega on arendatud mitmeid meetodeid ja indekseid, aitamaks disaneritel ning inseneridel välja selgitada parimad seadmed vastavate ülesannete lahendamiseks. Lisaks on huvi paralleelmehhanismide suunas viimaste aastate jooksul märgatavalt kasvanud. Peamiseks põhjuseks on paljudes valdkondades märgatavalt parem sooritusvõime võrreldes seriaalmanipulaatoritega. Ometi pole arendatud veel ühtegi globaalset jõudlusindeksit, mis võimaldaks täpsuse perspektiivis paralleelmanipulaatorite omavahelise võrdluse. Käesoleva lõputöö fookuseks on kintestaatilise jõuldusindeksi arendustööst ülevaate pakkumine. Uuritav indeks peab robustselt suutma hinnata läbi vastava indeksi paralleelmanipulaatorite täpsust.For as long as we have used robots there has also been ongoing research to allow us to use and improve efficiency of automation in our daily lives. As our knowledge about robots has largely improved, so has the complexity of their structures. Thus, various methods and indices have been developed to help designers and engineers determine the best manipulator for a specific task. In addition, the interest towards parallel manipulators has seen growth in the last couple of years due to significantly better performance in various areas in comparison to serial mechanisms. However, no global performance index to evaluate accuracy and allow comparison in that perspective between parallel mechanisms has been developed. This thesis focuses on giving an overview on the developments towards finding a robust kinematic sensitivity index to measure accuracy performance of parallel manipulators

Inverse Kinematic Analysis of Robot Manipulators

An important part of industrial robot manipulators is to achieve desired position and orientation of end effector or tool so as to complete the pre-specified task. To achieve the above stated goal one should have the sound knowledge of inverse kinematic problem. The problem of getting inverse kinematic solution has been on the outline of various researchers and is deliberated as thorough researched and mature problem. There are many fields of applications of robot manipulators to execute the given tasks such as material handling, pick-n-place, planetary and undersea explorations, space manipulation, and hazardous field etc. Moreover, medical field robotics catches applications in rehabilitation and surgery that involve kinematic, dynamic and control operations. Therefore, industrial robot manipulators are required to have proper knowledge of its joint variables as well as understanding of kinematic parameters. The motion of the end effector or manipulator is controlled by their joint actuator and this produces the required motion in each joints. Therefore, the controller should always supply an accurate value of joint variables analogous to the end effector position. Even though industrial robots are in the advanced stage, some of the basic problems in kinematics are still unsolved and constitute an active focus for research. Among these unsolved problems, the direct kinematics problem for parallel mechanism and inverse kinematics for serial chains constitute a decent share of research domain. The forward kinematics of robot manipulator is simpler problem and it has unique or closed form solution. The forward kinematics can be given by the conversion of joint space to Cartesian space of the manipulator. On the other hand inverse kinematics can be determined by the conversion of Cartesian space to joint space. The inverse kinematic of the robot manipulator does not provide the closed form solution. Hence, industrial manipulator can achieve a desired task or end effector position in more than one configuration. Therefore, to achieve exact solution of the joint variables has been the main concern to the researchers. A brief introduction of industrial robot manipulators, evolution and classification is presented. The basic configurations of robot manipulator are demonstrated and their benefits and drawbacks are deliberated along with the applications. The difficulties to solve forward and inverse kinematics of robot manipulator are discussed and solution of inverse kinematic is introduced through conventional methods. In order to accomplish the desired objective of the work and attain the solution of inverse kinematic problem an efficient study of the existing tools and techniques has been done. A review of literature survey and various tools used to solve inverse kinematic problem on different aspects is discussed. The various approaches of inverse kinematic solution is categorized in four sections namely structural analysis of mechanism, conventional approaches, intelligence or soft computing approaches and optimization based approaches. A portion of important and more significant literatures are thoroughly discussed and brief investigation is made on conclusions and gaps with respect to the inverse kinematic solution of industrial robot manipulators. Based on the survey of tools and techniques used for the kinematic analysis the broad objective of the present research work is presented as; to carry out the kinematic analyses of different configurations of industrial robot manipulators. The mathematical modelling of selected robot manipulator using existing tools and techniques has to be made for the comparative study of proposed method. On the other hand, development of new algorithm and their mathematical modelling for the solution of inverse kinematic problem has to be made for the analysis of quality and efficiency of the obtained solutions. Therefore, the study of appropriate tools and techniques used for the solution of inverse kinematic problems and comparison with proposed method is considered. Moreover, recommendation of the appropriate method for the solution of inverse kinematic problem is presented in the work. Apart from the forward kinematic analysis, the inverse kinematic analysis is quite complex, due to its non-linear formulations and having multiple solutions. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network (ANN) can be gainfully used to yield the desired results. Therefore, in the present work several models of artificial neural network (ANN) are used for the solution of the inverse kinematic problem. This model of ANN does not rely on higher mathematical formulations and are adept to solve NP-hard, non-linear and higher degree of polynomial equations. Although intelligent approaches are not new in this field but some selected models of ANN and their hybridization has been presented for the comparative evaluation of inverse kinematic. The hybridization scheme of ANN and an investigation has been made on accuracies of adopted algorithms. On the other hand, any Optimization algorithms which are capable of solving various multimodal functions can be implemented to solve the inverse kinematic problem. To overcome the problem of conventional tool and intelligent based method the optimization based approach can be implemented. In general, the optimization based approaches are more stable and often converge to the global solution. The major problem of ANN based approaches are its slow convergence and often stuck in local optimum point. Therefore, in present work different optimization based approaches are considered. The formulation of the objective function and associated constrained are discussed thoroughly. The comparison of all adopted algorithms on the basis of number of solutions, mathematical operations and computational time has been presented. The thesis concludes the summary with contributions and scope of the future research work

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

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

Creating robotic characters for long-term interaction

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-181).Researchers studying ways in which humans and robots interact in social settings have a problem: they don't have a robot to use. There is a need for a socially expressive robot that can be deployed outside of a laboratory and support remote operation and data collection. This work aims to fill that need with DragonBot - a platform for social robotics specifically designed for long-term interactions. This thesis is divided into two parts. The first part describes the design and implementation of the hardware, software, and aesthetics of the DragonBot-based characters. Through the use of a mobile phone as the robot's primary computational device, we aim to drive down the hardware cost and increase the availability of robots "in the wild". The second part of this work takes an initial step towards evaluating DragonBot's effectiveness through interactions with children. We describe two different teleoperation interfaces for allowing a human to control DragonBot's behavior differing amounts of autonomy by the robot. A human subject study was conducted and these interfaces were compared through a sticker sharing task between the robot and children aged four to seven. Our results show that when a human operator is able to focus on the social portions of an interaction and the robot is given more autonomy, children treat the character more like a peer. This is indicated by the fact that more children re-engaged the robot with the higher level of autonomy when they were asked to split up stickers between the two participants.by Adam Setapen.S.M

Planning simultaneous perception and manipulation

This thesis is concerned with deriving planning algorithms for robot manipulators. Manipulation has two effects, the robot has a physical effect on the object, and it also acquires information about the object. This thesis presents algorithms that treat both problems. First, I present an extension of the well-known piano mover’s problem where a robot pushing an object must plan its movements as well as those of the object. This requires simultaneous planning in the joint space of the robot and the configuration space of the object, in contrast to the original problem which only requires planning in the latter space. The effects of a robot action on the object configuration are determined by the non-invertible rigid body mechanics. To solve this a two-level planner is presented that coordinates planning in each space. Second, I consider planning under uncertainty and in particular planning for information effects. I consider the case where a robot has to reach and grasp an object under pose uncertainty caused by shape incompleteness. The main novel outcome is to enable tactile information gain planning for a dexterous, highdegree of freedom manipulator with non- Gaussian pose uncertainty. The method is demonstrated in trials with both simulated and real robots

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Improved Industrial Robot Positional Accuracy for Machining with Bias Correction

Robotic machining has the potential to provide advantages as a substitute for conventional CNC machine tool operations. However, conventional industrial robots are restricted to low accuracy tasks due to their poor positional accuracy. This creates challenges in achieving the tolerances required for machining tasks. Data-based modelling of the positional error data is a potential solution which learns the positional errors in order to compensate and minimise them. There has been some success in improving industrial robot accuracy in research literature, by first calibrating the kinematic model and then using machine learning (ML)-based bias correction to learn the positional errors. However, the limitations of ML-based bias correction applied to the industrial robot positional accuracy problem have not been fully explored with the accuracies required to achieve tight machining tolerances. Mapping the positional errors with a greater resolution of training data, and reducing the burden on bias correction by calibrating the kinematic model with a higher level of calibration, are two examples which have the potential to improve accuracy. This thesis focusses on both training data resolution and bias reduction to maximise outcomes whilst informing trade-offs when using ML-based bias correction in this application. The key finding of this thesis is that substantial gains in accuracy can be achieved using ML-based bias correction and that the accuracy limit can be achieved with practicable amounts of data gathering and processing. Also that calibration prior to bias correction did not significantly improve overall accuracy for the cases investigated. This suggests that data may be better utilised in training the bias corrector rather than for calibration of the physical model. In conclusion, ML-based bias correction methods can provide a solution that provides substantial gains in positional accuracy for conventional industrial robots, bringing them to a level that may facilitate broader adoption in machining applications

The Fifth NASA/DOD Controls-Structures Interaction Technology Conference, part 2

This publication is a compilation of the papers presented at the Fifth NASA/DoD Controls-Structures Interaction (CSI) Technology Conference held in Lake Tahoe, Nevada, March 3-5, 1992. The conference, which was jointly sponsored by the NASA Office of Aeronautics and Space Technology and the Department of Defense, was organized by the NASA Langley Research Center. The purpose of this conference was to report to industry, academia, and government agencies on the current status of controls-structures interaction technology. The agenda covered ground testing, integrated design, analysis, flight experiments and concepts