4 research outputs found
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
Estimation of boiler's tubes life, Artificial Neural Networks approach / Murtadha F. Muhsen
The analysis of creep-damage processes is becoming more and more important in engineering practice due to the fact that the exploitation condition like temperature and pressure are increasing while the weight of the structure should decrease. In the same time the safety standards are increasing too. The accuracy of the mechanical state estimation (stresses, strains and displacements) mainly depends on the introduced constitutive equations and on the chosen structural analysis model. This paper is devoted to the prediction of boiler s tube life using of Artificial Neural Network (ANN) technique. Training data used were obtained from Kapar power station technical reports. Predicted values of the remnant tube life were compared to the experimentally collected data to verify the success of the algorithm; average absolute error obtained was 1.667%. Results obtained show that the designed network is capable of predicting the remnant life of the boilers tube successfully. Predicting boilers tube life successfully presented using this method will help maintenance engineers to schedule preventive maintenance procedure in order to minimize maintenance cost and to prevent any consequences of disasters which may happen if the necessary precautions were not taken
Dynamic state estimation for mobile robots
The scientific goal of this thesis is to tackle different approaches for effective state estimation and modelling of relevant problems in the context of mobile robots. The starting point of this dissertation is the concept of probabilistic robotics, an emerging paradigm that combines state-of-the-art methods with the classic probabilistic theory, developing stochastic frameworks for understanding the uncertain nature of the interaction between a robot and its environment. This allows introducing relevant concepts which are the foundation of the localisation system implemented on the main experimental platform used on this dissertation. An accurate estimation of the position of a robot with respect to a fixed frame is fundamental for building navigation systems that can work in dynamic unstructured environments. This development also allows introducing additional contributions related with
global localisation, dynamic obstacle avoidance, path planning and position tracking problems.
Kinematics on generalised manipulators are characterised for dealing with complex nonlinear systems. Nonlinear formulations are needed to properly model these systems, which are not always suitable for real-time realisation, lacking analytic formulations in most cases. In this context, this thesis tackles the serial-parallel dual kinematic problem with a novel approach, demonstrating state-of-the-art accuracy and real-time performance. With a spatial decomposition method, the forward kinematics problem on parallel robots and the inverse kinematics problem on serial manipulators is solved modelling the nonlinear behaviour of the pose space using Support Vector Machines. The results are validated on different topologies with the analytic solution for such manipulators, which demonstrates the applicability of the proposed method.
Modelling and control of complex dynamical systems is another relevant field with applications on mobile robots. Nonlinear techniques are usually applied to tackle problems like feature or object tracking. However, some nonlinear integer techniques applied for tasks like position tracking in mobile robots with complex dynamics have limited success when modelling such systems. Fractional calculus has demonstrated to be suitable to model complex processes like viscoelasticity or super diffusion. These tools, that take advantage of the generalization of the derivative and integral operators to a fractional order, have been applied to model and control different topics related with robotics in recent years with remarkable success.
With the proposal of a fractional-order PI controller, a suitable controller design method is presented to solve the position tracking problem. This is applied to control the distance of a self-driving car with respect to an objective, which can also be applied to other tracking applications like following a navigation path. Furthermore, this thesis introduces a novel fractional-order hyperchaotic system, stabilised with a full-pseudo-state-feedback controller and a located feedback method. This theoretical contribution of a chaotic system is introduced hoping to be useful in this context. Chaos theory has recently started to be applied to study manipulators, biped robots and autonomous navigation, achieving new and promising results, highlighting the uncertain and chaotic nature which also has been found on robots.
All together, this thesis is devoted to different problems related with dynamic state estimation for mobile robots, proposing specific contributions related with modelling and control of complex nonlinear systems. These findings are presented in the context of a self-driving electric car, Verdino, jointly developed in collaboration with the Robotics Group of Universidad de La Laguna (GRULL)