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

Hand-Gesture Based Programming of Industrial Robot Manipulators

Nowadays, industrial robot manipulators and manufacturing processes are associated as never before. Robot manipulators execute repetitive tasks with increased accuracy and speed, features necessary for industries with needs for manufacturing of products in large quantities by reducing the production time. Although robot manipulators have a significant role for the enhancement of productivity within industries, the programming process of the robot manipulators is an important drawback. Traditional programming methodologies requires robot programming experts and are time consuming. This thesis work aims to develop an application for programming industrial robot manipulators excluding the need of traditional programing methodologies exploiting the intuitiveness of humans’ hands’ gestures. The development of input devices for intuitive Human-Machine Interactions provides the possibility to capture such gestures. Hence, the need of the need of robot manipulator programming experts can be replaced by task experts. In addition, the integration of intuitive means of interaction can reduce be also reduced. The components to capture the hands’ operators’ gestures are a data glove and a precise hand-tracking device. The robot manipulator imitates the motion that human operator performs with the hand, in terms of position. Inverse kinematics are applied to enhance the programming of robot manipulators in-dependently of their structure and manufacturer and researching the possibility for optimizing the programmed robot paths. Finally, a Human-Machine Interface contributes in the programming process by offering important information for the programming process and the status of the integrated components

Reconfigurable Validation Model for Identifying Kinematic Singularities and Reach Conditions for Articulated Robots and Machine Tools

Automation has led to industrial robots facilitating a wide array of high speed, endurance, and precision operations undertaken in the manufacturing industry today. An acceptable level of functioning and control is therefore vital to the efficacy and successful implementation of such manipulators. This research presents a comprehensive analytical tool for downstream optimization of manipulator design, functionality, and performance. The proposed model is reconfigurable and allows for modelling and validation of different industrial robots. Unique 3D visual models for a manipulator workspace and kinematic singularities are developed to gain an understanding into the task space and reach conditions of the manipulator\u27s end-effector. The developed algorithm also presents a non-conventional and computationally inexpensive solution to the inverse kinematics problem through the use Artificial Neural Networks. Application of the proposed technique is further extended to aid in development of path planning models for a uniform, continuous, and singularity free motion

Arm Robot Manipulator Design and Control for Trajectory Tracking; a Review

Arm robot manipulator heavily applied in industries ranging from welding, pick-and-place, assembly, packaging, labeling, etc. Trajectory planning and tracking is the fundamental design of an arm robot manipulator. The trajectory is set and determined to satisfy a certain criterion effectively and optimally. Optimization of robot trajectory is necessary to ensure the good quality product and to save energy, and this optimization can be provided by the right modeling and design. This paper presents a review study of arm-robot manipulator design and control for trajectory tracking by investigating the modeling of an arm robot manipulator starting from kinematics, dynamics and the application of the more advanced methods. The idea of this paper comes from the popularity of inverse kinematics among students

Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%