Verification of bee algorithm based path planning for a 6DOF manipulator using ADAMS

In this article the end effector displacement control for a manipulator robot with 6 rotational joints on a predetermined 3-dimensional trajectory is investigated. Since for any end effector position there are more than a single set of answers, regarding to robot parts orientation, finding a method which gives the designer all existing states will lead to more freedom of action. Hence two different methods were applied to solve robot inverse kinematic issue. In the first method ADAMS software was considered, which is a well-known software in the field of solving inverse kinematic problems, and after that BA algorithm is used as an intelligent method. This method is one of the fastest and most efficient methods among all existing ones for solving non-linear problems. Hence problem of inverse kinematic solution is transformed into an affair of optimization. Comparison of results obtained by both models indicates the reasonable performance of BA because of its capability in providing the answers from all existing states along with the privilege of no need to 3D modeling

Inverse Kinematic Solutions of Dual Redundant Camera Robot Based on Genetic Algorithm

Inverse kinematic solutions for a dual redundant camera robot in position are examined in order to alleviate operation difficulty and reduce time. The inverse kinematic algorithm is based on a basic genetic algorithm, and the genetic algorithm which is used to solve the problem of a redundant robot is mainly optimized in the joint space. On this basis, the genetic algorithm improvement strategies are studied. In this paper, a genetic algorithm with constrained 2 redundant degrees of freedom (DOF) is proposed through setting 2 parameter variables, with more flexible structure of optimization objective function and more efficient algorithm than basic genetic algorithm. Finally, the result of inverse kinematic algorithm is achieved in terms of the physical prototype

Prediction of Inverse Kinematics Solution of a Redundant manipulator using ANFIS

In this thesis, a method for forward and inverse kinematics analysis of a 5-DOF and a 7-DOF Redundant manipulator is proposed. Obtaining the trajectory and computing the required joint angles for a higher DOF robot manipulator is one of the important concerns in robot kinematics and control. When a robotic system possesses more degree of freedom (DOF) than those required to execute a given task is called Redundant Manipulator. The difficulties in solving the inverse kinematics (IK) equations of these redundant robot manipulator arises due to the presence of uncertain, time varying and non-linear nature of equations having transcendental functions. In this thesis, the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) is used to the generated data for solving inverse kinematics problem. The proposed hybrid neuro-fuzzy system combines the learning capabilities of neural networks with fuzzy inference system for nonlinear function approximation. A single-output Sugeno-type FIS (Fuzzy Inference System) using grid partitioning has been modeled in this work. The Denavit-Hartenberg (D-H) representation is used to model robot links and solve the transformation matrices of each joint. The forward kinematics and inverse kinematics for a 5-DOF and 7-DOF manipulator are analyzed systemically. ANFIS have been successfully used for prediction of IKs of 5-DOF and 7-DOF Redundant manipulator in this work. After comparing the output, it is concluded that the predicting ability of ANFIS is excellent as this approach provides a general frame work for combination of NN and fuzzy logic. The Efficiency of ANFIS can be concluded by observing the surface plot, residual plot and normal probability plot. This current study in using different nonlinear models for the prediction of the IKs of a 5-DOF and 7-DOF Redundant manipulator will give a valuable source of information for other modellers

Using symmetries in reinforcement learning of bimanual robotic tasks

The learning of bimanual robotic tasks, i.e., tasks executed by two manipulators together, can be particularly important in the new scenarios opened by the rise of humanoid robotics, one of the most interesting trend currently in the field. The work presented wants to build a method to simplify the dimensionality of parameter space in this particular context, exploiting the presence of symmetries between the movements executed by the two arms. The aim is to develop a reduced-order representation of the bimanual motion, with the purpose of increase the speed of learning process. In chapter 1, kinematics of the used robots is studied, in order to know how to correctly command the position of the robots while executing a task. Robotic movements are then modeled using Probabilistic Movement Primitives (ProMPs), a stochastic interpretation of robot movements (details in chapter 2). The first objective is to develop a symmetrization method for those kind of policies, and this part is treated in chapter 3. This will give the chance of representing the movement of two robotic arms, with only a single ProMP (instead of two, one for each arm), from which obtain the second policy applying symmetrization. In this way the amount of parameters representing motion can be halved. The most common kind of symmetry is the one defined by a plane, but also other cases can be explored, e.g., spherical or cylindrical symmetry. If the symmetry surface is not explicitly given in the bimanual task description, it is critical to have a reliable method to estimate it in order to exploit it in the learning process. In chapter 4 it is reported a way to obtain this estimation of the parameters describing the symmetry surface from the initially demonstrated trajectories. Finally, in chapter 5 it is defined a symmetric policy representation for bimanual task, that depends only on a single ProMP and a symmetry surface. The effectiveness of this parameter reduction has been tested applying it in reinforcement learning of some tasks, in comparison to the results obtained by the standard way of proceeding, that model the bimanual task with two separated ProMPs, one for each robotic arm

An Overview of Kinematic and Calibration Models Using Internal/External Sensors or Constraints to Improve the Behavior of Spatial Parallel Mechanisms

This paper presents an overview of the literature on kinematic and calibration models of parallel mechanisms, the influence of sensors in the mechanism accuracy and parallel mechanisms used as sensors. The most relevant classifications to obtain and solve kinematic models and to identify geometric and non-geometric parameters in the calibration of parallel robots are discussed, examining the advantages and disadvantages of each method, presenting new trends and identifying unsolved problems. This overview tries to answer and show the solutions developed by the most up-to-date research to some of the most frequent questions that appear in the modelling of a parallel mechanism, such as how to measure, the number of sensors and necessary configurations, the type and influence of errors or the number of necessary parameters

Dynamics and Motion of a Six Degree of Freedom Robot Manipulator

In this thesis, a strategy to accomplish pick-and-place operations using a six degree-of-freedom (DOF) robotic arm attached to a wheeled mobile robot is presented. This research work is part of a bigger project in developing a robotic-assisted nursing to be used in medical settings. The significance of this project relies on the increasing demand for elderly and disabled skilled care assistance which nowadays has become insufficient. Strong efforts have been made to incorporate technology to fulfill these needs. Several methods were implemented to make a 6-DOF manipulator capable of performing pick-and-place operations. Some of these methods were used to achieve specific tasks such as: solving the inverse kinematics problem, or planning a collision-free path. Other methods, such as forward kinematics description, workspace evaluation, and dexterity analysis, were used to describe the manipulator and its capabilities. The manipulator was accurately described by obtaining the link transformation matrices from each joint using the Denavit-Hartenberg (DH) notations. An Iterative Inverse Kinematics method (IIK) was used to find multiple configurations for the manipulator along a given path. The IIK method was based on the specific geometric characteristic of the manipulator, in which several joints share a common plane. To find admissible solutions along the path, the workspace of the manipulator was considered. Algebraic formulations to obtain the specific workspace of the 6-DOF manipulator on the Cartesian coordinate space were derived from the singular configurations of the manipulator. Local dexterity analysis was also required to identify possible orientations of the end-effector for specific Cartesian coordinate positions. The closed-form expressions for the range of such orientations were derived by adapting an existing dexterity method. Two methods were implemented to plan the free-collision path needed to move an object from one place to another without colliding with an obstacle. Via-points were added to avoid the robot mobile platform and the zones in which the manipulator presented motion difficulties. Finally, the segments located between initial, final, and via-points positions, were connected using straight lines forming a global path. To form the collision-free path, the straight-line were modified to avoid the obstacles that intersected the path. The effectiveness of the proposed analysis was verified by comparing simulation and experimental results. Three predefined paths were used to evaluate the IIK method. Ten different scenarios with different number and pattern of obstacles were used to verify the efficiency of the entire path planning algorithm. Overall results confirmed the efficiency of the implemented methods for performing pick-and-place operations with a 6-DOF manipulator

On-the-Fly Workspace Visualization for Redundant Manipulators

This thesis explores the possibilities of on-line workspace rendering for redundant robotic manipulators via parallelized computation on the graphics card. Several visualization schemes for different workspace types are devised, implemented and evaluated. Possible applications are visual support for the operation of manipulators, fast workspace analyses in time-critical scenarios and interactive workspace exploration for design and comparison of robots and tools

Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results