Modeling and Control of a Piezo-Actuated High-Dynamic Compensation Mechanism for Industrial Robots

This paper presents a method for modeling and control of a piezo-actuated high-dynamic compensation mechanism (HDCM) for usage together with an industrial robot during a machining operation, such as milling in aluminum. The spindle is attached to the compensation mechanism and the robot holds the workpiece. Due to the inherent resonant character of mechanical constructions of this type, and the nonlinear phenomena appearing in piezo actuators, control of the compensation mechanism is a challenging problem. This paper presents models of the construction, experimentally identified using subspace-based identification methods. A subsequent control scheme, based on the identified models, utilizing state feedback for controlling the position of the spindle is outlined. Experimental results performed on a prototype of the HDCM are also provided

Control Strategies for Machining with Industrial Robots

This thesis presents methods for improving machining with industrial robots using control, with focus on increasing positioning accuracy and controlling feed rate. The strong process forces arising during high-speed machining operations, combined with the limited stiffness of industrial robots, have hampered the usage of industrial robots in high-end machining tasks. However, since such manipulators may offer flexible and cost-effective machining solutions compared to conventional machine tools, it is of interest to increase the achievable accuracy using industrial robots. In this thesis, several different methods to increase the machining accuracy are presented. Modeling and control of a piezo-actuated high-dynamic compensation mechanism for usage together with an industrial robot during a machining operation, such as milling in aluminium, is considered. Position control results from experiments are provided, as well as an experimental verification of the benefit of utilizing the online compensation scheme. It is shown that the milling surface accuracy achieved with the proposed compensation mechanism is increased by up to three times compared to the uncompensated case. Because of the limited workspace and the higher bandwidth of the compensator compared to the robot, a mid-ranging approach for control of the relative position between the robot and the compensator is proposed. An adaptive, model-based solution is presented, which is verified through simulations as well as experiments, where a close correspondence with the simulations was achieved. Comparing the IAE from experiments using the proposed controller to previously established methods, a performance increase of up to 56 % is obtained. Additionally, two different approaches to increasing the accuracy of the machining task are also presented in this thesis. The first method is based on identifying a stiffness model of the robot, and using online force measurements in order to modify the position of the robot to compensate for position deflections. The second approach uses online measurements from an optical tracking system to suppress position deviations. In milling experiments performed in aluminium, the absolute accuracy was increased by up to a factor of approximately 6 and 9, for the two approaches, respectively. Robotic machining is often performed using position feedback with a conservative feed rate, to avoid excessive process forces. By controlling the applied force, realized by adjusting the feed rate of the workpiece, precise control over the material removal can be exercised. This will in turn lead to maximization of the time-efficiency of the machining task, since the maximum amount of material can be removed per time unit. This thesis presents an adaptive force controller, based on a derived model of the machining process and an identified model of the Cartesian dynamics of the robot. The controller is evaluated in both simulation and an experimental setup

Topics in Machining with Industrial Robots and Optimal Control of Vehicles

Two main topics are considered in this thesis: Machining with industrial robots and optimal control of road-vehicles in critical maneuvers. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of strong process forces affecting the robot end-effector. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, significantly degrade the achieved position accuracy of the machined part. In this thesis, a macro/micro manipulator configuration is considered to the purpose of increasing the position accuracy. In particular, a model-based control architecture is developed for control of the micro manipulator. The macro/micro manipulator configuration are validated by experimental results from milling tests in aluminium. The main result is that the proposed actuator configuration, combined with the control architecture proposed in this thesis, can be used for increasing the accuracy of industrial machining processes with robots. The interest for research on optimal control of road-vehicles in timecritical maneuvers is mainly driven by the desire to devise improved vehicle safety systems. Primarily, the solution of an optimal control problem for a specific cost function and model configuration can provide indication of performance limits as well as inspiration for control strategies in time-critical maneuvering situations. In this thesis, a methodology for solving this kind of problems is discussed. More specifically, vehicle and tire modeling and the optimization formulation required to get useful solutions to these problems are investigated. Simulation results are presented for different vehicle models, under varying road-surface conditions, in aggressive maneuvers, where in particular the tires are performing at their limits. The obtained results are evaluated and compared. The main conclusion here is that even simplified road-vehicle models are able to replicate behavior observed when experienced drivers are handling vehicles in time-critical maneuvers. Hence, it is plausible that the results presented in this thesis provide a basis for development of future optimization-based driver assistance technologies

Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis

Topics in Machining with Industrial Robot Manipulators and Optimal Motion Control

Two main topics are considered in this thesis: Machining with industrial robot manipulators and optimal motion control of robots and vehicles. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of the strong process forces required for the task. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, may significantly degrade the achieved machining accuracy of the manufactured part. In this thesis, a macro/micro-manipulator configuration is considered to the purpose of increasing the milling accuracy. In particular, a model-based control architecture is developed for control of the macro/micro-manipulator setup. The considered approach is validated by experimental results from extensive milling experiments in aluminium and steel. Related to the problem of high-accuracy milling is the topic of robot modeling. To this purpose, two different approaches are considered; modeling of the quasi-static joint dynamics and dynamic compliance modeling. The first problem is approached by an identification method for determining the joint stiffness and backlash. The second problem is approached by using gray-box identification based on subspace-identification methods. Both identification algorithms are evaluated experimentally. Finally, online state estimation is considered as a means to determine the workspace position and orientation of the robot tool. Kalman Filters and Rao-Blackwellized Particle Filters are employed to the purpose of sensor fusion of internal robot measurements and measurements from an inertial measurement unit for estimation of the desired states. The approaches considered are fully implemented and evaluated on experimental data. The second part of the thesis discusses optimal motion control applied to robot manipulators and road vehicles. A control architecture for online control of a robot manipulator in high-performance path tracking is developed, and the architecture is evaluated in extensive simulations. The main characteristic of the control strategy is that it combines coordinated feedback control along both the tangential and transversal directions of the path; this separation is achieved in the framework of natural coordinates. One motivation for research on optimal control of road vehicles in time-critical maneuvers is the desire to develop improved vehicle-safety systems. In this thesis, a method for solving optimal maneuvering problems using nonlinear optimization is discussed. More specifically, vehicle and tire modeling and the optimization formulations required to get useful solutions to these problems are investigated. The considered method is evaluated on different combinations of chassis and tire models, in maneuvers under different road conditions, and for investigation of optimal maneuvers in systems for electronic stability control. The obtained optimization results in simulations are evaluated and compared

Adaptation and Learning for Manipulators and Machining

This thesis presents methods for improving the accuracy and efficiency of tasks performed using different kinds of industrial manipulators, with a focus on the application of machining. Industrial robots offer a flexible and cost-efficient alternative to machine tools for machining, but cannot achieve as high accuracy out of the box. This is mainly caused by non-ideal properties in the robot joints such as backlash and compliance, in combination with the strong process forces that affect the robot during machining operations. In this thesis, three different approaches to improving the robotic machining accuracy are presented. First, a macro/micro-manipulator approach is considered, where an external compensation mechanism is used in combination with the robot, for compensation of high-frequency Cartesian errors. Two different milling scenarios are evaluated, where a significant increase in accuracy was obtained. The accuracy specification of 50 μm was reached for both scenarios. Because of the limited workspace and the higher bandwidth of the compensation mechanism compared to the robot, two different mid-ranging approaches for control of the relative position between the robot and the compensator are developed and evaluated. Second, modeling and identification of robot joints is considered. The proposed method relies on clamping the manipulator end effector and actuating the joints, while measuring joint motor torque and motor position. The joint stiffness and backlash can subsequently be extracted from the measurements, to be used for compensation of the deflections that occur during machining. Third, a model-based iterative learning control (ILC) approach is proposed, where feedback is provided from three different sensors of varying investment costs. Using position measurements from an optical tracking system, an error decrease of up to 84 % was obtained. Measurements of end-effector forces yielded an error decrease of 55 %, and a force-estimation method based on joint motor torques decreased the error by 38 %. Further investigation of ILC methods is considered for a different kind of manipulator, a marine vibrator, for the application of marine seismic acquisition. A frequency-domain ILC strategy is proposed, in order to attenuate undesired overtones and improve the tracking accuracy. The harmonics were suppressed after approximately 20 iterations of the ILC algorithm, and the absolute tracking error was r educed by a factor of approximately 50. The final problem considered in this thesis concerns increasing the efficiency of machining tasks, by minimizing cycle times. A force-control approach is proposed to maximize the feed rate, and a learning algorithm for path planning of the machining path is employed for the case of machining in non-isotropic materials, such as wood. The cycle time was decreased by 14 % with the use of force control, and on average an additional 28 % decrease was achieved by use of a learning algorithm. Furthermore, by means of reinforcement learning, the path-planning algorithm is refined to provide optimal solutions and to incorporate an increased number of machining directions

Miniaturized modular manipulator design for high precision assembly and manipulation tasks

In this paper, design and control issues for the development of miniaturized manipulators which are aimed to be used in high precision assembly and manipulation tasks are presented. The developed manipulators are size adapted devices, miniaturized versions of conventional robots based on well-known kinematic structures. 3 degrees of freedom (DOF) delta robot and a 2 DOF pantograph mechanism enhanced with a rotational axis at the tip and a Z axis actuating the whole mechanism are given as examples of study. These parallel mechanisms are designed and developed to be used in modular assembly systems for the realization of high precision assembly and manipulation tasks. In that sense, modularity is addressed as an important design consideration. The design procedures are given in details in order to provide solutions for miniaturization and experimental results are given to show the achieved performances

Design, Development, and Evaluation of a Teleoperated Master-Slave Surgical System for Breast Biopsy under Continuous MRI Guidance

The goal of this project is to design and develop a teleoperated master-slave surgical system that can potentially assist the physician in performing breast biopsy with a magnetic resonance imaging (MRI) compatible robotic system. MRI provides superior soft-tissue contrast compared to other imaging modalities such as computed tomography or ultrasound and is used for both diagnostic and therapeutic procedures. The strong magnetic field and the limited space inside the MRI bore, however, restrict direct means of breast biopsy while performing real-time imaging. Therefore, current breast biopsy procedures employ a blind targeting approach based on magnetic resonance (MR) images obtained a priori. Due to possible patient involuntary motion or inaccurate insertion through the registration grid, such approach could lead to tool tip positioning errors thereby affecting diagnostic accuracy and leading to a long and painful process, if repeated procedures are required. Hence, it is desired to develop the aforementioned teleoperation system to take advantages of real-time MR imaging and avoid multiple biopsy needle insertions, improving the procedure accuracy as well as reducing the sampling errors. The design, implementation, and evaluation of the teleoperation system is presented in this dissertation. A MRI-compatible slave robot is implemented, which consists of a 1 degree of freedom (DOF) needle driver, a 3-DOF parallel mechanism, and a 2-DOF X-Y stage. This slave robot is actuated with pneumatic cylinders through long transmission lines except the 1-DOF needle driver is actuated with a piezo motor. Pneumatic actuation through long transmission lines is then investigated using proportional pressure valves and controllers based on sliding mode control are presented. A dedicated master robot is also developed, and the kinematic map between the master and the slave robot is established. The two robots are integrated into a teleoperation system and a graphical user interface is developed to provide visual feedback to the physician. MRI experiment shows that the slave robot is MRI-compatible, and the ex vivo test shows over 85%success rate in targeting with the MRI-compatible robotic system. The success in performing in vivo animal experiments further confirm the potential of further developing the proposed robotic system for clinical applications

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