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

Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks

The values of a given manipulator's dynamics coefficients need to be accurately identified in order to employ model-based algorithms in the control of its motion. This thesis details the development of a novel form of adaptive network which is capable of accurately learning the coefficients of systems, such as manipulator inverse dynamics, where the algebraic form is known but the coefficients' values are not. Empirical motion data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear Combiner (CSLC) network developed, and the coefficients of their inverse dynamics identified. The resultant precision of control is shown to be superior to that achieved from employing dynamics coefficients derived from direct measurement. As part of the development of the CSLC network, the process of network learning is examined. This analysis reveals that current network architectures for processing analogue output systems with high input order are highly unlikely to produce solutions that are good estimates throughout the entire problem space. In contrast, the CSLC network is shown to generalise intrinsically as a result of its structure, whilst its training is greatly simplified by the presence of only one minima in the network's error hypersurface. Furthermore, a fine-tuning algorithm for network training is presented which takes advantage of the CSLC network's single adaptive layer structure and does not rely upon gradient descent of the network error hypersurface, which commonly slows the later stages of network training

Analyse de la stabilité de la coupe d'un procédé d'usinage robotisé

Productivity in robotic machining processes can be limited by the low rigidity of the overall structure and vibration instability (chatter). The robot’s dynamic behavior, due to changes in its posture along a machining trajectory, varies within its workspace. Chatter in robotic machining therefore depends not only on the cutting parameters but also on the robot configuration. The first objective of this thesis is to determine a dynamic modeling approach of the robot in order to analyze the vibration and the stability in robotic machining. This modeling approach has been realized to dynamic modeling of an ABB IRB6660 industrial robot. The numerical model parameters are adjusted on the basis of experimental modal identifications. Then, a three-dimensional representation of stability lobes diagram for the prediction to take into account the robot dynamic behavior variations in machining trajectory is established. The second objective is to optimize the robot configurations regarding stability. The dynamic behavior variations of the robot in the workspace are exploited through functional redundancy management in order to optimize robot configurations with respect to machining stability. The numerical analyze demonstrated and experimental machining tests confirmed that stability conditions in machining operations can be achieved by managing functional redundancy without changing the cutting parameters.La productivité des processus d'usinage robotisé est très souvent limitée par le manque de rigidité des robots et les problèmes vibratoires relatifs à l’instabilité de la coupe. L’analyse de l’instabilité de la coupe en usinage robotisé est un problème difficile en raison de la variabilité du comportement dynamique du robot dans son espace de travail. Par conséquent, le phénomène de broutement en usinage robotisé dépend non seulement des paramètres de coupe mais également de la configuration du robot. Le premier objectif de cette thèse est de déterminer une méthode de modélisation dynamique du robot, adaptée du point de vue de l’analyse des vibrations et de la stabilité en usinage robotisé. Cette approche a été réalisée sur le robot d’usinage industriel ABB IRB6660. Une démarche de recalage a été mise en place afin de déterminer les paramètres du modèle numérique du robot. Ensuite, une présentation 3D de la limite de stabilité en usinage robotisé prenant en compte les variations du comportement dynamique du robot est réalisée. Le deuxième objectif consiste à optimiser le procédé vis-à-vis de la stabilité de la coupe. Les variations du comportement dynamique du robot sont exploitées par la gestion des redondances fonctionnelles afin d’optimiser la configuration du robot du point de vue de la stabilité. L’analyse numérique a montré et les essais expérimentaux d’usinage ont confirmé la possibilité de passer de la zone instable à la zone stable par la gestion de la redondance fonctionnelle sans modifier les paramètres de coupe

Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bumblebees can detect floral humidity

Floral humidity, a region of elevated humidity in the headspace of the flower, occurs in many plant species and may add to their multimodal floral displays. So far, the ability to detect and respond to floral humidity cues has been only established for hawkmoths when they locate and extract nectar while hovering in front of some moth-pollinated flowers. To test whether floral humidity can be used by other more widespread generalist pollinators, we designed artificial flowers that presented biologically relevant levels of humidity similar to those shown by flowering plants. Bumblebees showed a spontaneous preference for flowers that produced higher floral humidity. Furthermore, learning experiments showed that bumblebees are able to use differences in floral humidity to distinguish between rewarding and non-rewarding flowers. Our results indicate that bumblebees are sensitive to different levels of floral humidity. In this way floral humidity can add to the information provided by flowers and could impact pollinator behaviour more significantly than previously thought