Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of ‘true’ parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem

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

Joint Dynamics and Adaptive Feedforward Control of Lightweight Industrial Robots

The use of lightweight strain-wave transmissions in collaborative industrial robots leads to structural compliance and a complex nonlinear behavior of the robot joints. Furthermore, wear and temperature changes lead to variations in the joint dynamics behavior over time. The immediate negative consequences are related to the performance of motion and force control, safety, and lead-through programming.This thesis introduces and investigates new methods to further increase the performance of collaborative industrial robots subject to complex nonlinear and time-varying joint dynamics behavior. Within this context, the techniques of mathematical modeling, system identification, and adaptive estimation and control are applied. The methods are experimentally validated using the collaborative industrial robots by Universal Robots.Mathematically, the robot and joint dynamics are considered as two coupled subsystems. The robot dynamics are derived and linearly parametrized to facilitate identification of the inertial parameters. Calibrating these parameters leads to improvements in torque prediction accuracy of 16.5 %-28.5 % depending on the motion.The joint dynamics are thoroughly analyzed and characterized. Based on a series of experiments, a comprehensive model of the robot joint is established taking into account the complex nonlinear dynamics of the strain-wave transmission, that is the nonlinear compliance, hysteresis, kinematic error, and friction. The steady-state friction is considered to depend on angular velocity, load torque, and temperature. The dynamic friction characteristics are described by an Extended Generalized Maxwell-Slip (E-GMS) model which describes in a combined framework; hysteresis characteristics that depend on angular position and Coulomb friction that depend on load torque. E-GMS model-based feedforward control improves the torque prediction accuracy by a factor 2.1 and improve the tracking error by a factor 1.5.An E-GMS model-based adaptive feedforward controller is developed to address the issue of friction changing with wear and temperature. The adaptive control strategy leads to improvements in torque prediction of 84 % and tracking error of 20 %

Modulation of mitotic progression and cell cycle checkpoints by phosphorylation-dependent protein-protein interactions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.Includes bibliographical references.Alteration of mitotic gene function has recently been discovered to play a key role in tumor formation and cancer progression through the induction of chromosomal aberrations and genomic instability. Polo-like-kinase-1 is a critical mitotic regulator, overexpressed in human tumors, that functions in mitotic entry after cellular stress, centrosome maturation, mitotic spindle control, and cytokinesis, which are all disregulated in cancer cells. To study the role of Polo-like kinases we took advantage of the recent discovery that the polo-box domain of Polo-like kinases is a phosphorylation-dependent binding module that regulates targeting of Polo-like kinases to their substrates. To identify the interactors of Polo-box domains we developed and performed a mitotic-specific yeast two hybrid and a pulldown mass spectrometry screen. This yielded a large number of specific interactors known to be involved in a vast variety of mitotic processes including those previously described to be involved in tumor progression. We demonstrate that Polo-like kinase regulation of cytokinesis-specific guanine-nucleotide exchange factors for the small G-protein Rho is necessary for proper actomyosin ring contraction and cytokinesis. Additionally we demonstrate that Polo-like-kinase-1 directly regulates the activity of the Rho-effector-kinase ROCK2, and thus Polo-like kinases modulate Rho signaling both upstream and downstream of Rho during cytokinesis. In addition to Polo-box domains we also worked on two other phosphorylation-dependent binding domains involved in cell cycle checkpoints that become disregulated in cancer cells, tandem BRCT domains and WW domains.(cont.) We examined the molecular basis for phosphorylation-dependent recognition by the tandem BRCT domains of BRCA1 through oriented-peptide-library screening and determination of an X-ray crystal structure of the domain bound to a phosphopeptide. This allowed us to rationalize why inherited mutations within the tandem BRCT domains of BRCA1 promote breast and ovarian cancer in humans. Secondly, we assayed WW domains that were generated from in silicon determined sequences for natural-like function to more fully understand the folding and binding requirements of this domain class. All three domains (tandem BRCT domains, Polo-box domains, and WW domains) are attractive targets for cancer therapeutics as they participate in control of processes necessary for genomic stability that become disregulated in cancer.by Drew M. Lowery.Ph.D