Development of Novel Task-Based Configuration Optimization Methodologies for Modular and Reconfigurable Robots Using Multi-Solution Inverse Kinematic Algorithms

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions

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

Solving Complex Geometry Kinematics Problem with Function Approximation by Artificial Neural Network

Neural networks have been widely deployed to solve classification and regression problems, and, in recent years, there has been much interest in using neural networks for solving complicated problems. In this paper, a hybrid manipulator kinematics is studied, and an artificial deep neural network is used to solve the inverse kinematics problem. The noise resistance benefit of neural networks approach is explored

A memetic approach to the inverse kinematics problem for robotic applications

The inverse kinematics problem of an articulated robot system refers to computing the joint configuration that places the end-effector at a given position and orientation. To overcome the numerical instability of the Jacobian-based algorithms around singular joint configurations, the inverse kinematics is formulated as a constrained minimization problem in the configuration space of the robot. In previous works this problem has been solved for redundant and non-redundant robots using evolutionary-based algorithms. However, despite the flexibility and accuracy of the direct search approach of evolutionary algorithms, these algorithms are not suitable for most robot applications given their low convergence speed rate and the high computational cost of their population-based approach. In this thesis, we propose a memetic variant of the Differential Evolution (DE) algorithm to increase its convergence speed on the kinematics inversion problem of articulated robot systems. With the aim to yield an efficient trade-off between exploration and exploitation of the search space, the memetic approach combines the global search scheme of the standard DE with an independent local search mechanisms, called discarding. The proposed scheme is tested on a simulation environment for different benchmark serial robot manipulators and anthropomorphic robot hands. Results show that the memetic differential evolution is able to find solutions with high accuracy in less generations than the original DE. -----------------------------------------------------------La cinemática inversa de los robots manipuladores se refiere al problema de calcular las coordenadas articulares del robot a partir de coordenadas conocidas de posición y orientación de su extremo libre. Para evitar la inestabilidad numérica de los métodos basados en la inversa de la matriz Jacobiana en la vecindad de configuraciones singulares, el problema de cinemática inversa es definido en el espacio de configuraciones del robot manipulador como un problema de optimización con restricciones. Este problema de optimización ha sido previamente resuelto con métodos evolutivos para robots manipuladores, redundantes y no redundantes, obteniéndose buenos resultados; sin embargo, estos métodos exhiben una baja velocidad de convergencia no adecuada para aplicaciones robóticas. Para incrementar la velocidad de convergencia de estos algoritmos, se propone un método memético de evolución differencial. El enfoque de búsqueda directa propuesto combina el esquema estándar de evolución diferencial con un mecanismo independiente de refinamiento local, llamado discarding o descarte. El desempeño del método propuesto es evaluado en un entorno de simulación para diferentes robot manipuladores y manos robóticas antropomórficas. Los resultados obtenidos muestran una importante mejora en precisión y velocidad de convergencia en comparación del método DE original.Programa en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Pedro M. Urbano de Almeida Lima; Vocal: Cecilia Elisabet García Cena; Secretario: Mohamed Abderrahim Fichouch

Hierarchical Adaptive Control of Modular and Reconfigurable Robot Manipulator Platforms

Within the rapidly growing interest in today's robotics industry, modular and reconfigurable robots (MRRs) are among the most auspicious systems to expand the adaptability of robotic applications. They are adaptable to multiple industrial field applications but they also have additional advantages such as versatile hardware, easier maintenance, and transportability. However, such features render the controller design that manages a variety of robot configurations with reliable performance more complex since their system dynamics involve not only nonlinearities and uncertainties but also changing dynamics parameters after the reconfiguration. In this thesis, the motion control problem of MRR manipulators is addressed and hierarchical adaptive control architecture is developed for MRRs. This hierarchical structure allows the adjustment of the nominal parameters of an MRR system for system parameter identification and control design purposes after the robot is reconfigured. This architecture simplifies the design of adaptive control for MRRs which is effective in the presence of dynamic parameter uncertainty, unmodeled dynamics, and disturbance. The proposed architecture provides flexibility in choosing adaptive algorithms applicable to MRRs. The developed architecture consists of high-level and low-level modules. The high-level module handles the dynamic parameters changes and reconstructs the parametric model used for on-line parameter identification after the modules are reassembled. The low-level structure consists of an adaptive algorithm updated by an on-line parameter estimation to handle the dynamic parameter uncertainties. Furthermore, a robust adaptive term is added into this low-level controller to compensate for the unmodeled dynamics and disturbances. The proposed adaptive control algorithms guarantee uniformly ultimate boundedness (UUB) of the MRR trajectories in terms of robust stability despite the dynamic parameter uncertainty, unmodeled dynamics, changes in the system dynamics, and disturbance

ALTERNATIVE AND FLEXIBLE CONTROL METHODS FOR ROBOTIC MANIPULATORS: On the challenge of developing a flexible control architecture that allows for controlling different manipulators

Robotic arms and cranes show some similarities in the way they operate and in the way they are designed. Both have a number of links serially attached to each other by means of joints that can be moved by some type of actuator. In both systems, the end-effector of the manipulator can be moved in space and be placed in any desired location within the system’s workspace and can carry a certain amount of load. However, traditional cranes are usually relatively big, stiff and heavy because they normally need to move heavy loads at low speeds, while industrial robots are ordinarily smaller, they usually move small masses and operate at relatively higher velocities. This is the reason why cranes are commonly actuated by hydraulic valves, while robotic arms are driven by servo motors, pneumatic or servo-pneumatic actuators. Most importantly, the fundamental difference between the two kinds of systems is that cranes are usually controlled by a human operator, joint by joint, using simple joysticks where each axis operates only one specific actuator, while robotic arms are commonly controlled by a central controller that controls and coordinates the actuators according to some specific algorithm. In other words, the controller of a crane is usually a human while the controller of a robotic arm is normally a computer program that is able to determine the joint values that provide a desired position or velocity for the end-effector. If we especially consider maritime cranes, compared with robotic arms, they rely on a much more complex model of the environment with which they interact. These kinds of cranes are in fact widely used to handle and transfer objects from large container ships to smaller lighters or to the quays of the harbours. Therefore, their control is always a challenging task, which involves many problems such as load sway, positioning accuracy, wave motion compensation and collision avoidance. Some of the similarities between robotic arms and cranes can also be extended to robotic hands. Indeed, from a kinematic point of view, a robotic hand consists of one or more kinematic chains fixed on a base. However, robotic hands usually present a higher number of degrees of freedom (DOFs) and consequentially a higher dexterity compared to robotic arms. Nevertheless, several commonalities can be found from a design and control point of views. Particularly, modular robotic hands are studied in this thesis from a design and control point of view. Emphasising these similarities, the general term of robotic manipulator is thereby used to refer to robotic arms, cranes and hands. In this work, efficient design methods for robotic manipulators are initially investigated. Successively, the possibility of developing a flexible control architecture that allows for controlling different manipulators by using a universal input device is outlined. The main challenge of doing this consists of finding a flexible way to map the normally fixed DOFs of the input controller to the variable DOFs of the specific manipulator to be controlled. This process has to be realised regardless of the differences in size, kinematic structure, body morphology, constraints and affordances. Different alternative control algorithms are investigated including effective approaches that do not assume a priori knowledge for the Inverse Kinematic (IK) models. These algorithms derive the kinematic properties from biologically-inspired approaches, machine learning procedures or optimisation methods. In this way, the system is able to automatically learn the kinematic properties of different manipulators. Finally, a methodology for performing experimental activities in the area of maritime cranes and robotic arm control is outlined. By combining the rapid-prototyping approach with the concept of interchangeable interfaces, a simulation and benchmarking framework for advanced control methods of maritime cranes and robotic arms is presented. From a control point of view, the advantages of releasing such a flexible control system rely on the possibility of controlling different manipulators by using the same framework and on the opportunity of testing different control approaches. Moreover, from a design point of view, rapidprototyping methods can be applied to fast develop new manipulators and to analyse different properties before making a physical prototype

Genetic-Based Optimisation Technique for the Development of Automated Inspection and Restoration Systems for Bridges

Automation and robotics are receiving significant attention in the field of inspection and restoration of steel bridges. However, the success level of the field implementations depends on numerous technological factors. This dissertation addresses aspects of the design, development and subsequent implementation of such on-site devices. The restoration process poses a high level of health hazard and carries environmental pollution risk. For these reasons, it is high on the consideration list for automation. The varied scale and geometry of bridges are some of the limiting conditions for performing the inspection and restoration tasks. Further aspects of concern are access provisions, the diversity of tasks required in the assessment and restoration of a bridge and compatibility between the operational characteristics of the automated device, tasks layout and direction of approach. The key factors, which arise as a result of the above analysis, are access, mobility, navigation, manipulation, probe change and control. In order to efficiently produce design alternatives, based on the industry (customers and designers) requirements, the engineering design framework is adopted. Due to the growing complexity of the required devices, new methodologies and approaches are needed. This dissertation presents a design methodology to generate alternatives for further considerations. The author's work combines: (i) research and suitability assessment of the existing enabling technologies, (ii) extensive task selection and analysis, (iii) incorporation of the industry requirements for generating the set of design criteria, and (iv) an innovative application of Genetic Algorithms. GA is used as a tool for simultaneous optimisation of the robot’s kinematic parameters, based on the criteria of collision and singularity avoidance, percentage of coverage, productivity and dexterity. Analysis and justification of a two-step approach is presented, with the former combining all the parameters, and the latter handling the chosen criteria. The methodology is then tested and verified on an existing construction robot (MPIR) from Technion. Finally, it is applied to two case studies, spherical and articulated manipulators performing a range of restoration activities on a selected bridge geometry model. A sensitivity analysis was also carried out on each case study in order to identify areas where improvements could be made. In general, the methodology is successful in choosing the more task-suitable manipulator and optimising the ranges of its kinematic parameters. This could be extended to optimise other parameters according to a set of alternative criteria. In doing so, it can bridge over several phases of the engineering design with a single approach

Design, modelling, and control of an ambidextrous robot arm

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The proposed arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) & Electric Motor)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) for the hand and modified Multiple Adaptive Neuro-fuzzy Inference System (MANFIS) controller for the arm). The primary challenge of the project was to achieve ambidextrous behavior of the arm. Thus, a feasibility analysis was carried out to evaluate possible mechanical designs. The secondary aim was to deal with control issues associated with the ambidextrous design. Due to the ambidextrous nature of the design, the stability of such a device becomes a challenging task. Conventional controllers and artificial intelligence-based controllers were explored to find the most suitable one. Performances of all these controllers have been compared through experiments, and combined use of PID with NN was found to be the most accurate controller to drive the ambidextrous robot hand. In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro-Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results, modified MANFIS is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with a conventional arm in terms of power consumption and stability

Evaluación de técnicas evolutivas en el cálculo de la cinemática inversa de robots manipuladores

En este proyecto, se examina el rendimiento de un algoritmo de búsqueda directa a partir del problema de cinemática inversa de robots manipuladores con diferentes grados de libertad. Para ello se plantea el siguiente problema de optimización: conocida la posición y orientación deseada en el extremo nal de un robot manipulador, y su función de cinemática directa, encontrar un posible vector de con guración articular óptimo dentro del espacio de trabajo del robot, de manera que, los errores de posición y orientación son mínimos. Un enfoque evolutivo, llamado Di erential Evolution, es el utilizado para resolver el anterior problema de optimización con restricciones no lineales. Di erential Evolution es un método de búsqueda directa estocástico basado en un proceso iterativo de mutación algebraica, cruce y selección sobre una población de soluciones candidatas. Basada en la experimentación empírica, este estudio pretende mostrar la in uencia de los valores de los parámetros de control (tamaño de la población, factor de escala de mutación, etc.) en el comportamiento del algoritmo para el problema de inversión cinemática. Finalmente, se introduce un operador de búsqueda local para mejorar la velocidad de convergencia del algoritmo. ______________________________________________________________________________________________________________________In this dissertation, the performance of a direct search algorithm is examined for the inverse kinematics problem of robot manipulators with di erent degrees of freedom. The following optimization model has been considered: given a desired end-e ector position and orientation, and the forward kinematics function of a robot manipulator, nd a feasible optimal joint con- guration vector within the robot's workspace, such that, the position and orientation errors are minimal. An evolutionary-based approach, called Di erential Evolution, is used to solve the above nonlinear constrained optimization problem. Di erential Evolution is a stochastic direct search method based on an iterative process of algebraic mutation, crossover, and selection over a population of candidate solutions. Based on empirical experimentation, the study aims to reveal the in uence of control parameters values (population size, mutation scale factor, etc.) on the algorithm behaviour for the kinematic inversion problem. Finally, a local search operator is introduced to improve the convergence speed of the algorithm.Ingeniería Técnica en Electrónic

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field