Neizrazito adaptivno upravljanje silom dodira slijednih mehanizama s jednim stupnjem slobode gibanja

The paper presents position/force control with a completely fuzzified adaptive force control system for the single degree of freedom servo mechanisms. The proposed force control scheme contains an adaptive fuzzy force controller and a subordinated fuzzy velocity controller. By using a second-order reference model, a model reference-based fuzzy adaptation mechanism is able to keep the error between the model and system output responses within desired limits. The results obtained by computer simulations indicate a stable performance of the force control system for a wide range of environment stiffness variations. The proposed adaptive force control method has also been effective in case of a contact with a rough surface or a complex form workpiece.Članak prikazuje upravljanje položajem/silom dodira slijednog mehanizma s jednim stupnjem slobode gibanja korištenjem neizrazitog adaptivnog sustava upravljanja silom. Predložena shema upravljanja silom dodira sadrži adaptivni neizraziti regulator sile i podređeni neizraziti regulator brzine vrtnje. Koristeći referentni model drugog reda, neizraziti na modelu zasnovani adaptacijski mehanizam u stanju je držati razliku između odziva modela i odziva sustava u zadanim granicama. Rezultati dobiveni numeričkim simulacijama pokazuju stabilno vladanje sustava upravljanja silom dodira za široki raspon varijacija krutosti okoline. Predložena metoda adaptivnog upravljanja silom se pokazala uspješnom i u slučaju dodira s neravnom površinom ili s radnim predmetom složena oblika

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Navigational control of multiple mobile robots in various environments

The thesis addresses the problem of mobile robots navigation in various cluttered environments and proposes methodologies based on a soft computing approach, concerning to three main techniques: Potential Field technique, Genetic Algorithm technique and Fuzzy Logic technique. The selected techniques along with their hybrid models, based on a mathematical support, solve the three main issues of path planning of robots such as environment representation, localization and navigation. The motivation of the thesis is based on some cutting edge issues for path planning and navigation capabilities, that retrieve the essential for various situations found in day-to-day life. For this purpose, complete algorithms are developed and analysed for standalone techniques and their hybrid models. In the potential field technique the local minima due to existence of dead cycle problem has been addressed and the possible solution for different situations has been carried out. In fuzzy logic technique the different controllers have been designed and their performance analysis has been done during their navigational control in various environments. Firstly, the fuzzy controller having all triangular members with five membership functions have been considered. Subsequently the membership functions are changed from Triangular to other functions, e.g. Trapezoidal, Gaussian functions and combinational form to have a more smooth and optimised control response. It has been found that the fuzzy controller with all Gaussian membership function works better compared to other chosen membership functions. Similarly the proposed Genetic algorithm is based on the suitable population size and fitness functions for finding out the robot steering angle in various cluttered field. At the end hybrid approaches e.g. Potential-Fuzzy, otential-Genetic, Fuzzy-Genetic and Potential-Fuzzy-Genetic are considered for navigation of multiple mobile robots. Initially the combination of two techniques has been selected in order to model the controllers and then all the techniques have been hybridized to get a better controller. These hybrid controllers are first designed and analysed for possible solutions for various situations provided by human intelligence. Then computer simulations have been executed extensively for various known and unknown environments. The proposed hybrid algorithms are embedded in the controllers of the real robots and tested in realistic scenarios to demonstrate the effectiveness of the developed controllers. Finally, the thesis concludes in a chapter describing the comparison of results acquired from various environments, showing that the developed algorithms achieve the main goals proposed by different approaches with a high level of simulations. The main contribution provided in the thesis is the definition and demonstration of the applicability of multiple mobile robots navigations with multiple targets in various environments based on the strategy of path optimisation

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

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

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Motion control of unmanned ground vehicle using artificial intelligence

The aim of this thesis is to solve two problems: the. trajectory tracking and navigation, for controlling the motion of unmanned ground vehicles (UGV). Such vehicles are usually used in industry for assisting automated production process or delivery services to improve and enhance the quality and efficiency. With regard to the trajectory tracking problem, the main task is to design a new method that is capable of minimising trajectory-tracking errors in UGV. To achieve this, a comprehensive mathematical model needs to be established that contains kinematic and dynamic characteristics beside actuators. In addition, different trajectories need to be generated and applied individually as a reference input, i.e. continuous gradient trajectories such as linear, circular and lemniscuses or a non-continuous gradient trajectory such as a square trajectory. The design method is based on a novel fractional order proportional integral derivative (FOPID) control strategy, which is proposed to control the movement of UGV to track given trajectories. Two FOPID controllers are required in this design. The first FOPID is constructed in order to control the orientation of UGV. The second FOPID controller is to control the speed of UGV. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters for both controllers. The significance of the proposed method is that an observable improvement has been achieved in terms of minimising trajectory-tracking errors and reducing control efforts, especially in continuous gradient trajectories. The stability of the proposed controllers is investigated based upon Nyquist stability criterion. Moreover, the robustness of the controllers is examined in the presence of disturbances to demonstrate the effectiveness of the controllers under certain harsh conditions. The influence from external disturbances has been represented by square pulses and sinusoidal waves. The drawback of this method, however, a highly trajectory tracking error is observed in non-continuous gradient trajectories due to the sharpness of the rotation at the corners of a square trajectory. To overcome this drawback, a new controller, abbreviated as (NN-FOPID), has been proposed based on a combination of neural networks and the FOPID. The purpose is to minimise the trajectory tracking error of non-continuous trajectories, in particular. The Levenberg-Marquardt (LM) algorithm is used to train the NN-FOPID controller. The neural networks’ cognitive capacities have made the system adaptable to respond effectively to the variants in trajectories. The obtained results by using NN-FOPID have shown a significant improvement of reducing errors of trajectory tracking and increasing control efforts over the results by FOPID. The other task is to solve the navigation problem of UGV in static and dynamic environments. This can be conducted by firstly constructing workspace environments that contain multiple dynamic and static obstacles. The dynamic obstructing obstacles can move in different velocities. The static obstacles can be randomly positioned in the workspace and all obstacles are allowed to have different sizes and shapes. Secondly, a UGV can be placed in any initial posture on the condition that it has to reach a given destination within the boundaries of the workspace. Thirdly, a method based on fuzzy inference systems (FIS) is proposed to control the motion of the UGV. The design of FIS is based on fuzzification, inference engine and defuzzification processes. The navigation task is divided into obstacle avoidance and target reaching tasks. Consequently, two individual FIS controllers are required to drive the actuators of the UGV, one is to avoid obstacles and the other is to reach a target. Both FIS controllers are combined through a switching mechanism to select the obstacle avoidance FIS controller if there is an obstacle, otherwise choosing reaching target FIS. The simulation results have confirmed the effectiveness of the proposed design in terms of obtaining optimal paths with shortest elapsed time. Similarly, a new method is proposed based on an adaptive neurofuzzy inference system (ANFIS) to guide the UGV in unstructured environments. This method combines the advantages of adaptive leaning and inference fuzzy system. The simulation results have demonstrated adequate achievements in terms of obtaining shortest and feasible paths whilst avoiding static obstructing obstacles and hence reaching the specified targets speedily. Finally, a UGV is constructed to investigate the overall performance of the proposed FIS controllers practically. The architecture of the UGV consists of three ultrasonic sensors, a magnetic compass and two quadratic decoders that they are interfaced with an Arduino microcontroller to read the sensory information. The Arduino, who acts as a slave microcontroller is serially connected with a master Raspberry Pi microcontroller. Raspberry Pi and Arduino communicate with each other based on a proposed hierarchical algorithm. Three case studies are introduced to demonstrate the effectiveness and the validation of the proposed FIS controllers and the UGV’s platform in real-time