Inverse Kinematics Based on Fuzzy Logic and Neural Networks for the WAM-Titan II Teleoperation System

The inverse kinematic problem is crucial for robotics. In this paper, a solution algorithm is presented using artificial intelligence to improve the pseudo-inverse Jacobian calculation for the 7-DOF Whole Arm Manipulator (WAM) and 6-DOF Titan II teleoperation system. An investigation of the inverse kinematics based on fuzzy logic and artificial neural networks for the teleoperation system was undertaken. Various methods such as Adaptive Neural-Fuzzy Inference System (ANFIS), Genetic Algorithms (GA), Multilayer Perceptrons (MLP) Feedforward Networks, Radial Basis Function Networks (RBF) and Generalized Regression Neural Networks (GRNN) were tested and simulated using MATLAB. Each method for identification of the pseudo-inverse problem was tested, and the best method was selected from the simulation results and the error analysis. From the results, the Multilayer Perceptrons with Levenberg-Marquardt (MLP-LM) method had the smallest error and the fastest computation among the other methods. For the WAM-Titan II teleoperation system, the new inverse kinematics calculations for the Titan II were simulated and analyzed using MATLAB. Finally, extensive C code for the alternative algorithm was developed, and the inverse kinematics based on the artificial neural network with LM method is implemented in the real system. The maximum error of Cartesian position was 1.3 inches, and from several trajectories, 75 % of time implementation was achieved compared to the conventional method. Because fast performance of a real time system in the teleoperation is vital, these results show that the new inverse kinematics method based on the MLP-LM is very successful with the acceptable error

Navigation of Automatic Vehicle using AI Techniques

In the field of mobile robot navigation have been studied as important task for the new generation of mobile robot i.e. Corobot. For this mobile robot navigation has been viewed for unknown environment. We consider the 4-wheeled vehicle (Corobot) for Path Planning, an autonomous robot and an obstacle and collision avoidance to be used in sensor based robot. We propose that the predefined distance from the robot to target and make the robot follow the target at this distance and improve the trajectory tracking characteristics. The robot will then navigate among these obstacles without hitting them and reach the specified goal point. For these goal achieving we use different techniques radial basis function and back-propagation algorithm under the study of neural network. In this Corobot a robotic arm are assembled and the kinematic analyses of Corobot arm and help of Phidget Control Panel a wheeled to be moved in both forward and reverse direction by 2-motor controller have to be done. Under kinematic analysis propose the relationships between the positions and orientation of the links of a manipulator. In these studies an artificial techniques and their control strategy are shown with potential applications in the fields of industry, security, defense, investigation, and others. Here finally, the simulation result using the webot neural network has been done and this result is compared with experimental data for different training pattern

Intelligent Control Architecture For Motion Learning in Robotics Applications

Abstract: The investigation of this Thesis was focused on how motion abilities can be learned by a robot. The main goal was to design and test a control architecture capable of learning how to properly move different simulated robots, through the use of Arti�cial Intelligence (AI) methods. With this purpose, a simulation environment and a set of simulated robots were created in order to test the control architecture. The robots were constructed with a simple geometry using links and joints. A fuzzy controller was designed to control the motors position. The control architecture design was based on subsumption and some AI methods that allowed the simulated robot to find and learn a set of motions based on targets. These methods were a genetic algorithm (GA) and a set of artificial neural networks (ANN). The GA was used to find the adequate robot movements for an specific target, while the ANNs were used to learn and perform such movements eficiently. The advantage of this approach was that, no knowledge of the environment or robot model is needed. The robot learns how to move its own body in order to achieve a determined task. In addition, the learned motions can be used to achieve complex movement execution in a further research. A set of experiments were performed in the simulator in order to show the performance of the control architecture in every one of its stages. The results showed that the proposed architecture was able to learn and perform basic movements of a robot independently of the environment or the robot defined structure.En esta Tesis, se investiga cómo las habilidades de movimiento en un robot, pueden ser aprendidas de forma automática. El objetivo principal fue dise~nar y probar una arquitectura de control capaz de aprender a mover adecuadamente diferentes robots simulados, mediante el uso de métodos de Inteligencia Artificial (IA). Con este propósito, se dise~no un entorno de simulación y un conjunto de robots simulados con el fin de probar la arquitectura de control. Los robots fueron construidos con una geometría muy simple utilizando enlaces y uniones (actuadores), y un controlador difuso fue dise~nado para controlar la posición de los actuadores. El dise~no de la arquitectura de control se basa en el concepto de subsunción (subsumption) y algunos métodos de IA que permiten al robot simulado determinar y aprender una serie de movimientos basados en objetivos. Los métodos usados son un algoritmo genético (GA) y un conjunto de redes neuronales artificiales (ANN). El GA se utiliza para encontrar los movimientos adecuados que el robot debe realizar para alcanzar un objetico específico, mientras que las redes neuronales se utilizan para aprender y realizar estos movimientos de forma eficiente. La ventaja de este enfoque es que, no es necesario conocer el entorno o tener un modelo del robot, sino que el robot aprende cómo mover su propio cuerpo en un ambiente definido con el fin de lograr una tarea determinada. Además, en una posterior investigación, es posible utilizar los movimientos aprendidos para realizar movimientos o tareas más complejas con los robots. Un conjunto de experimentos se llevaron a cabo en el simulador para mostrar el desempe~no de la arquitectura de control en cada una de sus etapas. Los resultados muestran que la arquitectura propuesta es capaz de aprender y realizar los movimientos del robot independientemente del medio ambiente o la estructura definida del robot.Maestrí

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

The application of neural networks in active suspension

This thesis considers the application of neural networks to automotive suspension systems. In particular their ability to learn non-linear feedback control relationships. The speed of processing, once trained, means that neural networks open up new opportunities and allow increased complexity in the control strategies employed. The suitability of neural networks for this task is demonstrated here using multilayer perceptron, (MLP) feed forward neural networks applied to a quarter vehicle simulation model. Initially neural networks are trained from a training data set created using a non-linear optimal control strategy, the complexity of which prohibits its direct use. They are shown to be successful in learning the relationship between the current system states and the optimal control. [Continues.

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Neural-learning-based force sensorless admittance control for robots with input deadzone

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic manipulators in response to external torques from the unknown environment is achieved by admittance control. Inspired by broad learning system (BLS), a flatted neural network structure using Radial Basis Function (RBF) with incremental learning algorithm is proposed to estimate the external torque, which can avoid retraining process if the system is modelled insufficiently. To deal with uncertainties in the robot system, an adaptive neural controller with dynamic learning framework is developed to ensure the tracking performance. Experiments on the Baxter robot have been implemented to test the effectiveness of the proposed method

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