Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Strategies for attitude control of reconfigurable modular spacecraft

The purpose of this thesis is to propose, investigate and develop an innovative approach to the attitude control of Cubesat-sized, modular and variable-shape spacecraft. These systems could ideally comply with the requirements of a larger variety of in-orbit functions and better adapt to the needs of specific subsystems in achieving and maintaining the desired attitude. The reference array is assumed to consist of a certain number of modules interconnected by means of revolute joints. One interesting aspect, which is the specific focus of the present thesis, is that such system can be capable of exploiting the dynamic effect of momentum conserving internal torques generated by the modules rotating with respect to each other for reorientation purposes. Initial inspiration for this proposed approach to spacecraft attitude control design has been drawn from the study of the well known 'falling cat' problem. In the long term, this innovative attitude control methodology, could justify the increase in cost and complexity modular reconfigurable systems require not only with advantages in the added versatility with respect to the mission tasks but also with better performance in attitude control system efficiency, accuracy, stability and even robustness. Specifically, this thesis discusses the available information present in literature about momentum preserving attitude control of multibody arrays and possible space applications, builds and validates a tool for the investigation of the peculiarities of these systems and finally investigates their non-linear behaviour for both the 2D and 3D cases. With respect to previous work in the field, optimal attitude control trajectories that take into account module impingement are discussed and the dynamics of momentum-preserving manoeuvres is analysed from the physical and mathematical points of view for both 2D and 3D manoeuvres. The results of the analysis demonstrate the validity of the concept and highlighted some the potentialities but also the critical points for a further development of the technology

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Robot Calibration Using Artificial Neural Networks

Robot calibration is an integrated procedure of measurement and data processing to improve and maintain robot positioning accuracy. Existing robot calibration techniques require extensive human intervention and off-line processing, which preclude the techniques from being used to perform on-site calibration in an industrial environment at regular intervals. This thesis investigates and develops intelligent calibration processing algorithms and a novel measurement method toward rapid autonomous robot calibration in a shop-floor environment.Artificial Neural Network (ANN) techniques have been vigorously investigated for calibration data processing (modelling, identification and compensation). A new identification algorithm has been developed for estimating robot kinematic parameter errors using Hopfield continuous-valued type Recurrent Neural Network (RNN). The RNN-based algorithm is computationally more efficient and robust compared with conventional optimisation approaches.A generic accuracy model which accounts for various error sources was introduced. A higher-order neural network was used for implementation of the generic accuracy model. Due to the ANN learning capability, computational power and adaptability, the ANN-based accuracy representation offers an appealing solution to the complex modelling problem.Efficient and robust accuracy compensation algorithms have been developed under the framework of artificial neural networks. The ANN-based algorithms provide constant-time inverse compensation therefore are suitable for on-line implementation. Both path compensation and compensation near robot singularity were tackled using the new algorithm.A novel autonomous calibration tool was developed using a trigger probe and a constraint plane. The new method eliminates any use of external measuring devices to determine robot end-effector location measurements, enabling the robot to perform self-calibration on a production line. Robot accuracy was improved to the level of its repeatability within the local calibration volume using the new calibration scheme, which is consistent with the results from using a precision external measuring device, in this case a Coordinate Measuring Machine (CMM)

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Restricted structure non-linear generalized minimum variance control

This research presents the Restricted Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm for Linear Parameter-Varying (LPV) systems. The LPV systems are defined as linear plant subsystems within the control diagram and may include Non-linear (NL) input subsystems. The RS-NGMV control solution for the latter will be slightly different than the first one and have the capability of dealing with NL characteristics such as saturation, discontinuities and black-box terms. The controller is built in a low-order Restricted Structure (RS) in the form of a general z-transfer function. This brings forward two major advantages. First, it offers a high-order advanced control solution inside low-order control structures which are known for their natural robustness. Secondly, it is easier to operate and re-tune for the classically trained staff in the industry as it can be given the structures they are rather familiar with such as the PID. Another advantage of the RS-NGMV is its model-based design that enables a faster adaptation to implement different systems. Features of the RS-NGMV are investigated throughout the thesis with case studies from trends in engineering like robotics, autonomous and electric vehicles. The results show that the RS-NGMV is highly capable of adapting to set-point changes, parameter variations with its ability to update the control gains rapidly by using optimizations. Some extensions of algorithms have also been studied following recent directions in optimal/predictive control resulting in a new preview control approach and Scheduled RS-NGMV control.This research presents the Restricted Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm for Linear Parameter-Varying (LPV) systems. The LPV systems are defined as linear plant subsystems within the control diagram and may include Non-linear (NL) input subsystems. The RS-NGMV control solution for the latter will be slightly different than the first one and have the capability of dealing with NL characteristics such as saturation, discontinuities and black-box terms. The controller is built in a low-order Restricted Structure (RS) in the form of a general z-transfer function. This brings forward two major advantages. First, it offers a high-order advanced control solution inside low-order control structures which are known for their natural robustness. Secondly, it is easier to operate and re-tune for the classically trained staff in the industry as it can be given the structures they are rather familiar with such as the PID. Another advantage of the RS-NGMV is its model-based design that enables a faster adaptation to implement different systems. Features of the RS-NGMV are investigated throughout the thesis with case studies from trends in engineering like robotics, autonomous and electric vehicles. The results show that the RS-NGMV is highly capable of adapting to set-point changes, parameter variations with its ability to update the control gains rapidly by using optimizations. Some extensions of algorithms have also been studied following recent directions in optimal/predictive control resulting in a new preview control approach and Scheduled RS-NGMV control