Neural Networks Technique for the Control of Artificial Mobile Agents

This project presents a neural network approach to the motion control of an artificial mobile robot. The robot is required to move towards its goal in a cluttered environment and simultaneously avoiding collision with obstacles of any size and being randomly distributed. We have used feed-forward back propagation algorithm to control the robot motion. Our main focus is on the steering action of the robot. The algorithm takes input as angle to target, front obstacle distance, right obstacle distance and left obstacle distance. Sensors have been used for the purpose of getting the inputs on various obstacle distances. The network provides the angle through which the robot is to be steered to avoid obstacles. The steering is achieved by controlling the speed of motors and hence of wheels to whom they are attached to. The viability of the algorithm has been demonstrated through various simulations. The real-world experimental outcomes prove the efficacy of the algorithm to navigate the robot successfully

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

Parallel Manipulators

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications

An intelligent manufacturing system for heat treatment scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Function Design of Mechatronic Systems for Human-Robot Collaboration

Traditionally, robots have been caged off from human activity but, recently, improvements in advance robotic technology as well as the introduction of new safety standards, have allowed the possibility of collaboration between human workers and robotic systems. The introduction of Human-Robot Collaboration has the potential to increase the quality and the flexibility of the production process while improving the working condition of the operators. However, traditional industrial robots are typically characterized by small payload and small reachable workspace that reduce the range of possible applications. These drawbacks can overcome the advantages related to a collaborative task and make the collaboration not effective. This work aims at analyzing innovative mechatronic solutions capable of increasing the workspace and the versatility of the system with the final goal of creating effective collaborations with humans. Cable driven Parallel Robots (CDPRs) are considered a promising technology able to satisfy these requirements. In fact, compared to rigid serial and parallel robots, they have several advantages such as large workspaces, high payloads per unit of weight, ease of construction, versatility and affordable costs. This work presents two innovative solutions of CDPR able to enlarge the workspace, improve the versatility and reduce the collisions risk. The first solution consists of a cable-suspended parallel robot with a reconfigurable end-effector whereas the second solution is an innovative model of cable-driven micro-macro robot. In the first part of the thesis, the kinematic and dynamic models of these innovative systems are presented and analyzed in order to characterize their capability. Trajectory planning and optimal design are addressed with the purpose of maximizing the performance of the systems. The last part of the thesis deals with the design of a novel family of Intelligent CAble-driven parallel roBOTs whose architecture and control are conceived to maximize the robot versatility to the task to be performed and the environment in which the robot is intended to operate

An intelligent manufacturing system for heat treatment scheduling

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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