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K nahrazení člověka při úloze testování dotykových embedded zařízení je zapotřebí vyvinout komplexní automatizovaný robotický systém. Jedním ze zásadních úkolů je tento systém automaticky zkalibrovat. V této práci jsem se zabýval možnými způsoby automatické kalibrace robotického ramene v prostoru ve vztahu k dotykovému zařízení pomocí jedné či více kamer. Následně jsem představil řešení založené na estimaci polohy jedné kamery pomocí iterativních metod jako např. Gauss-Newton nebo Levenberg-Marquardt. Na konci jsem zhodnotil dosaženou přesnost a navrhnul postup pro její zvýšení.

The need for humanity to lessen its impact on the ecologies we’re a part of is becoming increasingly clear.  While there are many technically advanced solutions in the makings, one of the most straightforward ones is to simply use the objects we surround ourselves with longer. We can make them more durable and repairable but what happens when the needs of the user changes? How can we really know what needs the future will bring? I’m searching for a design approach that embraces this unknown. A process where the designer doesn’t have to be the enlightened figure that knows everything in advance. Because if we accept the complexity of the social and ecological reality where our objects end up, we never can be. I want to make objects that can grow and change with the user. A fertile soil for future needs. I’ve used the design process of three different objects to develop these thoughts.  An open ended loom for the weaver Vega Määttä Siltberg, a table made with designer Julie Amira that is disassemblable without tools, and lastly a piece of furniture that changes function without moving parts. The goal of this project is not to produce a solution to a specific problem or to create an object that communicates an idea as well as possible. Rather it’s the reverse. By the process of designing and building I’ve been able to explore the questions that my project is composed of. The thoughts expressed here come from this experience.

A contribuição e objetivo desta tese é desenvolver um modelo de redes neurais artificiais, baseado em princípios de memória associativa, capaz de resolver o problema de planejamento e controle ponto a ponto de trajetória de um braço mecânico imerso em um ambiente parcialmente conhecido e/ou sujeito a ruídos. O modelo proposto é formado por dois planos: plano seqüência temporal e plano ângulo. Para o plano seqüência temporal, o novo modelo proposto chamado de Memória Associativa Multidirecional Temporal (TMAM) é capaz de armazenar e recuperar n-tuplas de informações, lidar com informações ruidosas e/ou incompletas e aprender seqüências temporais. TMAM utiliza representação contínua e realimentação autoassociativa. O plano ângulo é formado pelo modelo RBF que é responsável por produzir as informações de ângulos das juntas do braço mecânico. A composição dos dois planos forma o sistema completo que é responsável pelo planejamento e controle ponto a ponto de trajetória. Em resumo, o sistema recebe informações do ponto origem e do ponto alvo, estabelece uma trajetória para atingir o ponto alvo a partir do ponto de origem e transforma os pontos espaciais da trajetória em valores de ângulos das juntas. Os resultados obtidos mostram que o modelo TMAM é capaz de recuperar, interpelar e extrapolar pontos nas seqüências, é capaz de gerar trajetórias, de memorizar seqüências de diferentes tamanhos e de lidar com duas trajetórias ao mesmo tempo. O modelo apresenta também rápido treinamento. O modelo RBF é capaz de recuperar as saídas desejadas apresentando um erro pequeno e é capaz de receber um padrão que apresenta um ponto final inatingível e gerar um conjunto de ângulos que representa um ponto final atingível. / The aim of this project is to develop an artificial neural networks model based on principles of associative memory. This neural network model must be able to solve the problem of trajectory planning and point to point control of a robot arm, which is located in a partially known and/or noisy environment. The proposed model is composed by two surfaces: the temporal sequence surface and the angle surface. For the temporal sequence surface the new propose model Temporal Multidirectional Associative Memmy (TMAM) is able to store and recall n-tuplas of information, to deal with noisy and/or incomplete information and to learn temporal sequences. TMAM uses a continuas representation and autoassociative feedback. A RBF model is used to implement the angle surface, which is liable for producing the angle information for the joint of the robot arm. The two surfaces compose the whole system which is liable for the trajectory planning and system control. Hence, the system receives information about the initial point and the target point, constructs the trajectory to reach the target point from the initial point and converts the spatial points which compose the trajectory, in values of joint angles. The obtained results show that TMAM model can recall, interpolate and extrapolate points in the sequences. The model has the ability of generating new trajectories and memorizing different size of sequences at the same time. This model also shows fast learning. The RBF model can recall the desired outputs with a small error and can receive a pattern which is formed by an unreachable final point and generate a set of angles which, in turn, represent a reachable final point.

A contribuição e objetivo desta tese é desenvolver um modelo de redes neurais artificiais, baseado em princípios de memória associativa, capaz de resolver o problema de planejamento e controle ponto a ponto de trajetória de um braço mecânico imerso em um ambiente parcialmente conhecido e/ou sujeito a ruídos. O modelo proposto é formado por dois planos: plano seqüência temporal e plano ângulo. Para o plano seqüência temporal, o novo modelo proposto chamado de Memória Associativa Multidirecional Temporal (TMAM) é capaz de armazenar e recuperar n-tuplas de informações, lidar com informações ruidosas e/ou incompletas e aprender seqüências temporais. TMAM utiliza representação contínua e realimentação autoassociativa. O plano ângulo é formado pelo modelo RBF que é responsável por produzir as informações de ângulos das juntas do braço mecânico. A composição dos dois planos forma o sistema completo que é responsável pelo planejamento e controle ponto a ponto de trajetória. Em resumo, o sistema recebe informações do ponto origem e do ponto alvo, estabelece uma trajetória para atingir o ponto alvo a partir do ponto de origem e transforma os pontos espaciais da trajetória em valores de ângulos das juntas. Os resultados obtidos mostram que o modelo TMAM é capaz de recuperar, interpelar e extrapolar pontos nas seqüências, é capaz de gerar trajetórias, de memorizar seqüências de diferentes tamanhos e de lidar com duas trajetórias ao mesmo tempo. O modelo apresenta também rápido treinamento. O modelo RBF é capaz de recuperar as saídas desejadas apresentando um erro pequeno e é capaz de receber um padrão que apresenta um ponto final inatingível e gerar um conjunto de ângulos que representa um ponto final atingível. / The aim of this project is to develop an artificial neural networks model based on principles of associative memory. This neural network model must be able to solve the problem of trajectory planning and point to point control of a robot arm, which is located in a partially known and/or noisy environment. The proposed model is composed by two surfaces: the temporal sequence surface and the angle surface. For the temporal sequence surface the new propose model Temporal Multidirectional Associative Memmy (TMAM) is able to store and recall n-tuplas of information, to deal with noisy and/or incomplete information and to learn temporal sequences. TMAM uses a continuas representation and autoassociative feedback. A RBF model is used to implement the angle surface, which is liable for producing the angle information for the joint of the robot arm. The two surfaces compose the whole system which is liable for the trajectory planning and system control. Hence, the system receives information about the initial point and the target point, constructs the trajectory to reach the target point from the initial point and converts the spatial points which compose the trajectory, in values of joint angles. The obtained results show that TMAM model can recall, interpolate and extrapolate points in the sequences. The model has the ability of generating new trajectories and memorizing different size of sequences at the same time. This model also shows fast learning. The RBF model can recall the desired outputs with a small error and can receive a pattern which is formed by an unreachable final point and generate a set of angles which, in turn, represent a reachable final point.

Robotic and control industry implements different control technique to control the movement and the position of a robotic arm. PID controllers are the most used controllers in the robotics and control industry because of its simplicity and easy implementation. However, PIDs’ performance suffers under noisy environments. In this research, a controller based on Artificial Neural Networks (ANN) called the model reference controller is examined to replace traditional PID controllers to control the position of a robotic arm in a noisy environment. Simulations and implementations of both controllers were carried out in MATLAB. The training of the ANN was also done in MATLAB using the Supervised Learning (SL) model and Levenberg-Marquardt backpropagation algorithm. Results shows that the ANN implementation performs better than traditional PID controllers in noisy environments. / Robotoch kontrollindustrin implementerar olika kontrolltekniker för att styra rörelsen och placeringen av en robotarm. PID-styrenheter är de mest använda kontrollerna inom roboten och kontrollindustrin på grund av dess enkelhet och lätt implementering. PID:s prestanda lider emellertid i bullriga miljöer. I denna undersökning undersöks en styrenhet baserad på Artificiell Neuralt Nätverk (ANN) som kallas modellreferenskontrollen för att ersätta traditionella PID-kontroller för att styra en robotarm i bullriga miljöer. Simuleringar och implementeringar av båda kontrollerna utfördes i MATLAB. Utbildningen av ANN:et gjordes också i MATLAB med hjälp av Supervised Learning (SL) -modellen och LevenbergMarquardt backpropagationsalgoritmen. Resultat visar att ANN-implementeringen fungerar bättre än traditionella PID-kontroller i bullriga miljöer.

The world is constantly changing and workers are forced to adapt to a pace that is more suitable for machines. In the Automotive Industry assembly line workers injuries keeps increasing despite major improvements in recent years. This is due to the cause that the marked demands more goods but to a lower cost. With this in mind, how can we create an aid for assembly line workers that could decrease repetitive strain injuries, facilitate flexibility and reduce the weight load on the workers joints? In this project I have explored the possibility to create new and intelligent aids for the assembly lines with great focus the human body and ergonomics.

Tesis por compendio / [ES] El mecanizado con brazos robots ha sido estudiado aproximadamente desde los años 90, durante este tiempo se han llevado a cabo importantes avances y descubrimientos en cuanto a su campo de aplicación. En general, los robots manipuladores tienen muchos beneficios y ventajas al ser usados en operaciones de mecanizado, tales como, flexibilidad, gran área de trabajo y facilidad de programación, entre otras, frente a las Máquinas Herramientas de Control numérico (MHCN) que necesitan de una gran inversión para trabajar piezas muy grandes o incrementar sus grados de libertad. Como desventajas, frente a las MHCN, los brazos robóticos poseen menor rigidez, lo que combinado con las altas fuerzas producidas en los procesos de mecanizado hace que aparezcan errores de precisión, desviaciones en las trayectorias, vibraciones y, por consiguiente, una mala calidad en las piezas fabricadas. Entre los brazos robots, los brazos colaborativos están en auge debido a su programación intuitiva y a sus medidas de seguridad, que les permiten trabajar en el mismo espacio que los operadores sin que estos corran riesgos. Como desventaja añadida de los robots colaborativos se encuentra la mayor flexibilidad que estos tienen en sus articulaciones, debido a que incluyen reductores del tipo Harmonic drive. El uso de un control de fuerza en procesos de mecanizado con brazos robots permite controlar y corregir en tiempo real las desviaciones generadas por la flexibilidad en las articulaciones del robot. Utilizar este método de control es beneficioso en cualquier brazo robot; sin embargo, el control interno que incluyen los robots colaborativos presenta ventajas que permiten que el control de fuerza pueda ser aplicado de una manera más eficiente. En el presente trabajo se desarrolla una propuesta real para la inclusión del control de esfuerzos en el brazo robot, así como también, se evalúa y cuantifica la capacidad de los robots industriales y colaborativos en tareas de mecanizado. La propuesta plantea cómo mejorar la utilización de un control de fuerza por bucle interior/exterior aplicado en un brazo colaborativo cuando se desconocen los pares reales de los motores del robot, así como otros parámetros internos que los fabricantes no dan a conocer. Este bucle de control interior/exterior ha sido utilizado en aplicaciones de pulido y lijado sobre diferentes materiales. Los resultados indican que el robot colaborativo es factible para realizar tales operaciones de mecanizado. Sus mejores resultados se obtienen cuando se utiliza un bucle de control interno por velocidad y un bucle de control externo de fuerza con algoritmos, Proporcional-Integral-Derivativo o Proporcional más Pre-Alimentación de la Fuerza. / [CA] El mecanitzat amb braços robots ha estat estudiat aproximadament des dels anys 90, durant aquest temps s'han dut a terme importants avanços i descobriments en el que fa al seu camp d'aplicació. En general, els robots manipuladors tenen molts beneficis i avantatges al ser usats en operacions de mecanitzat, com ara, flexibilitat, gran àrea de treball i facilitat de programació, entre d'altres, davant de Màquines Eines de Control Numèric (MECN) que necessiten d'una gran inversió per treballar peces molt grans o incrementar els seus graus de llibertat. Com a desavantatges, enfront de les MECN, els braços robòtics posseeixen menor rigidesa, el que combinat amb les altes forces produïdes en els processos de mecanitzat fa que apareguin errors de precisió, desviacions en les trajectòries, vibracions i, per tant, una mala qualitat en les peces fabricades. Entre els braços robots, els braços col·laboratius estan en auge a causa de la seva programació intuïtiva i a les seves mesures de seguretat, que els permeten treballar en el mateix espai que els operadors sense que aquests corrin riscos. Com desavantatge afegida als robots col·laboratius es troba la major flexibilitat que aquests tenen en les seves articulacions, a causa de que inclouen reductors del tipus Harmonic drive. L'ús d'un control de força en processos de mecanitzat amb braços robots permet controlar, i corregir, en temps real les desviacions generades per la flexibilitat en les articulacions del robot. Utilitzar aquest mètode de control és beneficiós en qualsevol braç robot, però, el control intern que inclouen els robots col·laboratius presenta avantatges que permeten que el control de força es puga aplicar d'una manera més eficient. En el present treball es desenvolupa una proposta real per a la inclusió del control d'esforços en el braç robot, així com s'avalua i quantifica la capacitat dels robots industrials i col·laboratius en tasques de mecanitzat. La proposta planteja com millorar la utilització d'un control de força per bucle interior/exterior aplicat en un braç col·laboratiu, quan es desconeixen els parells reals dels motors del robot, així com altres paràmetres interns que els fabricants no donen a conèixer. Aquest bucle de control interior/exterior ha estat utilitzat en aplicacions de polit sobre diferents materials. Els resultats indiquen que el robot col·laboratiu és factible de realitzar aquestes operacions de mecanitzat. Els seus millors resultats s'obtenen quan s'utilitza un bucle de control intern per velocitat i un bucle de control extern de força amb els algoritmes Proporcional-Integral-Derivatiu o Proporcional més Pre-alimentació de la Força. / [EN] Machining with robot arms has been studied approximately since the 90s; during this time, important advances and discoveries have been made in its field of application. In general, manipulative robots have many benefits and advantages when they are used in machining operations, such as flexibility, large work area, and ease of programming, among others, compared to Numerical Control Machine Tools (NCMT) that need a great investment to work very large pieces or increase their degrees of freedom. As for disadvantages, compared to NCMT, robotic arms have lower rigidity, which, combined with the high forces produced in machining processes, causes precision errors, path deviations, vibrations, and, consequently, poor quality in the manufactured parts. Among robot arms, collaborative arms are on the rise due to their intuitive programming and safety measures, which allow them to work in the same space without risk for the operators. An added disadvantage of collaborative robots is their flexibility in their joints because they include Harmonic drive type reducers. The use of force control in machining processes with robot arms makes possible to control and correct, in real-time, the deviations generated by the flexibility in the robot's joints. The use of this control method is beneficial for any robot arm. However, the internal control included in collaborative robots has advantages that allow the force control to be applied more efficiently. In this work, a real proposal is developed to include effort control in the robot arm. The capacity of industrial and collaborative robots in machining tasks is evaluated and quantified. The proposal recommends how to improve the use of an inner/outer force control loop applied in a collaborative arm, when the real torques of the robot's motors are unknown and other internal parameters that manufacturers do not disclose. This inner/outer control loop has been used in polishing and sanding applications on different materials. The results indicate that the collaborative robot is feasible to perform such machining operations. Best results are obtained using an internal velocity control loop and external force control loop with Proportional-Integral-Derivative or Proportional plus Feed Forward. / The authors are grateful for the financial support of the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE). This work was funded by the CONICYT PFCHA/DOCTORADO BECAS CHILE/2017 – 72180157. / Pérez Ubeda, RA. (2022). Propuesta de inclusión de esfuerzos en el control de un brazo robot para asegurar el cumplimiento de la rugosidad superficial durante operaciones de lijado en diferentes materiales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182000 / Compendio