20 research outputs found

    A Flexible Piezoresistive/Self-Capacitive Hybrid Force and Proximity Sensor to Interface Collaborative Robots

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    Force and proximity sensors are key in robotics, especially when applied in collaborative robots that interact physically or cognitively with humans in real unstructured environments. However, most existing sensors for use in robotics are limited by: 1) their scope, measuring single parameters/events and often requiring multiple types of sensors, 2) being expensive to manufacture, limiting their use to where they are strictly necessary and often compromising redundancy, and 3) have null or reduced physical flexibility, requiring further costs with adaptation to a variety of robot structures. This paper presents a novel mechanically flexible force and proximity hybrid sensor based on piezoresistive and self-capacitive phenomena. The sensor is inexpensive and easy to apply even on complex-shaped robot structures. The manufacturing process is described, including controlling circuits, mechanical design, and data acquisition. Experimental trials featuring the characterisation of the sensor were conducted, focusing on both force-electrical resistance and self-capacitive proximity response. The sensor's versatility, flexibility, thinness (1 mm thickness), accuracy (reduced drift) and repeatability demonstrated its applicability in several domains. Finally, the sensor was successfully applied in two distinct situations: hand guiding a robot (by touch commands), and human-robot collision avoidance (by proximity detection)

    Event-based tracking of human hands

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    This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space)

    Robot dynamics: A recursive algorithm for efficient calculation of Christoffel symbols

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    Christoffel symbols of the first kind are very important in robot dynamics. They are used for tuning various proposed robot controllers, for determining the bounds on Coriolis/Centrifugal matrix, for mathematical formulation of optimal trajectory calculation, among others. In the literature of robot dynamics, Christoffel symbols of the first kind are calculated from Lagrangian dynamics using an off-line generated symbolic formula. In this study we present a novel and efficient recursive, non-symbolic, method where Christoffel symbols of the first kind are calculated on-the-fly based on the inertial parameters of robot’s links and their transformation matrices. The proposed method was analyzed in terms of execution time, computational complexity and numerical error. Results show that the proposed algorithm compares favorably with existing methods

    On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case

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    Human–robot collision avoidance is a key in collaborative robotics and in the framework of Industry 4.0. It plays an important role for achieving safety criteria while having humans and machines working side-by-side in unstructured and time-varying environment. This study introduces the subject of manipulator’s on-line collision avoidance into a real industrial application implementing typical sensors and a commonly used collaborative industrial manipulator, KUKA iiwa. In the proposed methodology, the human co-worker and the robot are represented by geometric primitives (capsules). The minimum distance and relative velocity between them is calculated, when human/obstacles are nearby the concept of hypothetical repulsion and attraction vectors is used. By coupling this concept with a mathematical representation of robot’s kinematics, a task level control with collision avoidance capability is achieved. Consequently, the off-line generated nominal path of the industrial task is modified on-the-fly so the robot is able to avoid collision with the co-worker safely while being able to fulfill the industrial operation. To guarantee motion continuity when switching between different tasks, the notion of repulsion-vector-reshaping is introduced. Tests on an assembly robotic cell in automotive industry show that the robot moves smoothly and avoids collisions successfully by adjusting the off-line generated nominal paths

    Reducing the Computational Complexity of Mass-Matrix Calculation for High DOF Robots

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    Increasingly, robots have more degrees of freedom (DOF), imposing a need for calculating more complex dynamics. As a result, better efficiency in carrying out dynamics computations is becoming more important. In this study, an efficient method for computing the joint space inertia matrix (JSIM) for high DOF serially linked robots is addressed. We call this method the Geometric Dynamics Algorithm for High number of robot Joints (GDAHJ). GDAHJ is non-symbolic, preserve simple formulation, and it is convenient for numerical implementation. This is achieved by simplifying the way to recursively derive the mass-matrix exploiting the unique property of each column of the JSIM and minimizing the number of operations with O(n2) complexity. Results compare favorably with existing methods, achieving better performance over state-of-the-art by Featherstone when applied for robots with more than 13 DOF

    Efficient Calculation of Minimum Distance Between Capsules and Its Use in Robotics

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    The problem of minimum distance calculation between line-segments/capsules, in 3D space, is an important subject in many engineering applications, spanning CAD design, computer graphics, simulation, and robotics. In the latter, the human robot minimum distance is the main input for collision avoidance/detection algorithms to measure collision imminence. Capsules can be used to represent humans and objects, including robots, in a given dynamic environment. In this scenario, it is important to calculate the minimum distance between capsules ef ciently, especially for scenes (situations) that include a high number of capsules. This paper investigates the utilization of QR factorization for performing ef cient minimum distance calculation between capsules. The problem is reformulated as a bounded variable optimization in which an af ne transformation, deduced from QR factorization, is applied on the region of feasible solutions. A geometrical approach is proposed to calculate the solution, which is achieved by computing the point closest to the origin from the transferred region of feasible solutions. This paper is concluded with numerical tests, showing that the proposed method compares favorably with the most ef cient method reported in the literature

    Collision Avoidance of Redundant Robotic Manipulators Using Newton’s Method

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    This study investigates the application of Newton method to the problems of collision avoidance and path planning for robotic manipulators, especially robots with high Degrees of Freedom (DOF). The proposed algorithm applies to the potential fields method, where the Newton technique is used for performing the optimization. As compared to classical gradient descent method this implementation is mathematically elegant, enhances the performance of motion generation, eliminates oscillations, does not require gains tuning, and gives a faster convergence to the solution. In addition, the paper presents a computationally efficient symbolic formula for calculating the Hessian with respect to joint angles, which is essential for achieving realtime performance of the algorithm in high DOF configuration spaces. The method is validated successfully in simulation environment. Results for different methods (Newton, gradient descent and gradient descent with momentum) are compared in terms of quality of the path generated, oscillations, minimum distance to obstacles and convergence rate

    Des robots manipulateurs collaboratifs sûrs

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    Collaborative industrial manipulators are ushering a new era in flexible manufacturing, where robots and humans are allowed to coexist and work side by side. However, various challenges still persist in achieving full human robot collaboration on the factory floor. In this thesis two main challenges - safety and collaboration - for achieving that goal are addressed. On safety, the thesis presents a real-time collision avoidance method which allows the robot to adjust the offline generated paths of the industrial task in real-time for avoiding collisions with humans nearby. In addition, the thesis presented a new method for performing the reactive collision avoidance motion using second order Newton method which offers various advantages over the traditional methods in the literature. On collaboration, the thesis presents the precision hand-guiding as an alternative to the teach-pendant for performing precise positioning operations of the robot’s end-effector in a simple and intuitive manner. The thesis also presents new contributions into the mathematical formulation of robot dynamics, including a recursive algorithm for calculating the mass matrix of serially linked robots with a minimal second order cost, and a recursive algorithm for calculating Christoffel symbols efficiently. All the presented algorithms are validated either in simulation or in a real-world scenario.Les manipulateurs industriels collaboratifs ouvrent une nouvelle ère dans la fabrication flexible, où les robots et les humains sont capables de coexister et de travailler ensemble. Cependant, divers défis persistent pour parvenir à une collaboration complète entre les robots et les humains en milieu industriel. Dans cette thèse, deux défis principaux - la sécurité et la collaboration - sont abordés pour atteindre cet objectif. Concernant la sécurité, la thèse présente une méthode d'évitement des collisions en temps réel qui permet au robot d'ajuster les chemins générés hors ligne pour une tâche industrielle, tout en évitant les collisions avec les humains à proximité. En outre, la thèse a présenté une nouvelle méthode pour effectuer un mouvement d'évitement de collision réactif, en utilisant la méthode de Newton du second ordre qui offre divers avantages par rapport aux méthodes traditionnelles utilisées dans la littérature. Sur la collaboration, la thèse présente un mode de guidage manuel précis comme alternative au mode de guidage actuel pour effectuer des opérations de positionnement précis de l'effecteur terminal du robot d’une manière simple et intuitive. La thèse présente également de nouvelles contributions à la formulation mathématique de la dynamique des robots, y compris un algorithme récursif pour calculer la matrice de masse des robots sériels avec un coût minimal du second ordre et un algorithme récursif pour calculer efficacement les symboles de Christoffel. Tous les algorithmes présentés sont validés soit en simulation, soit dans un scénario réel

    Safe Collaborative Robotic Manipulators

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    Tese no âmbito do Doutoramento em Engenharia Mecânica, Gestão e Robótica Industrial, apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.Collaborative robotic manipulators are new type of industrial manipulators, that allow the coexistence of humans in the same working area around the robot. Consequently, a human can work safely beside a collaborative manipulator, without having physical barriers or safety fences separating between them. In light of this, we can see that the main features of collaborative robots are centred around two main aspects, safety and collaboration, which are the main topics of this thesis. On the subject of safe interaction with the robot, we revisit the problem of realtime collision avoidance for robotic manipulators in unstructured and dynamic environments, where humans are working side-by-side with the robot. We propose a method for endowing the manipulator with sensing capabilities, so it can detect the proximity of humans around it. Also, we propose a control algorithm, based on the potential fields method, for providing the robot with human like reflexes, which allows it to avoid collision with people nearby. Afterwards, we propose a method for implementing the collision avoidance controller to modify on-line generated paths (necessary for performing industrial tasks). For testing, we implement the proposed method in a real industrial robotic cell, utilising an industry standard collaborative manipulator (KUKA iiwa) in human centred environment, where a human worker (coworker) and the robot are sharing the same working area. On the subject of collaboration we study the topic of precise positioning of the end-effector of the robot in an intuitive manner. We present the precision hand-guiding technique, and we present a lightweight algorithm for performing motion in the null space of redundant manipulators while preserving the accuracy of end-effector's pose. Afterwards, we exploit the redundancy for performing secondary tasks, where using the torque feedback at the joints of sensitive robots and the force/torque measurements from an external Force/Torque (FT) sensor at the flange of the robot we were able to perform precise hand-guiding at the end-effector level while exploiting redundancy to perform in-contact obstacle navigation. For developing the aforementioned applications, and for achieving real-time performance, we implemented various light weight numerical methods. Consequently, several efficient algorithms for performing the calculations and the control have been realized in software packages, most of which are provided as open source libraries in public repositories. Throughout this work, the proposed algorithms were implemented in various programming languages, while the real-time virtual-reality simulations were carried out using the program V-REP (Virtual Robot Experimentation Platform). Using these tools several controllers, algorithms, techniques and simulations were applied and the results achieved were discussed throughout this document. Experimental tests were carried out using industrial-grade hardware, the collaborative manipulator KUKA iiwa7R800, laser scanner, IMUs, force torque sensor and magnetic trackers.Os manipuladores robóticos colaborativos são um novo tipo de manipulador industrial que permite a coexistência de seres humanos na área de trabalho em redor do robô. Consequentemente, um humano pode trabalhar com segurança ao lado de um manipulador colaborativo, partilhando o mesmo espaço e sem barreiras físicas entre eles. As principais características dos robôs colaborativos centram-se em dois aspetos principais, segurança e colaboração, sendo estes os tópicos principais desta tese. Relativamente à interação segura com manipuladores robóticos colaborativos, revisitamos o problema da prevenção de colisões em tempo real em ambientes não estruturados e dinâmicos, onde humanos e robôs trabalham lado a lado. É proposto um método para dotar o manipulador de recursos de deteção de humanos/obstáculos em seu redor. Além disso, é proposto um algoritmo de controlo baseado no método de campos potenciais que permite ao robô colaborativo evitar colisões com humanos/obstáculos. Posteriormente, propomos um método para o controlador de prevenção de colisões de forma a alterar as trajetórias nominais do robô definidas off-line (necessário para executar tarefas industriais). As metodologias propostas foram implementadas e testadas numa célula robótica real, utilizando um manipulador colaborativo industrial (KUKA iiwa) em tarefas onde robô e humano partilham a mesma área de trabalho. O tópico da colaboração homem-robô foi estudado considerando o posicionamento preciso do robô de forma intuitiva. É proposto um método de guiamento manual de precisão, assente num algoritmo que permite o movimento do robô no espaço nulo de manipuladores redundantes enquanto preserva a precisão posicional. A redundância é também explorada recorrendo ao uso de medições de torque a partir das articulações do robô e medições de força/torque de um sensor externo de Força/Torque (FT) acoplado à flange do robô. Desta forma conseguimos realizar o guiamento manual do robô ao nível do end-effector enquanto tiramos vantagem da redundância na navegação de obstáculos por contato. As metodologias acima mencionadas foram implementadas em robôs reais, recorrendo a algoritmos que pela sua natureza de aplicação devem ser computacionalmente leves e eficientes, permitindo assim desempenho em tempo real por parte do sistema robótico. Neste contexto, vários algoritmos relacionados com a implementação dos métodos, cálculos matemáticos e controlo foram desenvolvidos e agrupados em pacotes de software, a maioria dos quais são fornecidos como bibliotecas de código aberto em repositórios públicos. Os algoritmos propostos foram implementados em várias linguagens de programação, enquanto as que simulações em tempo real foram realizadas usando o programa V-REP (Virtual Robot Experimentation Platform). Usando essas ferramentas vários controladores e algoritmos foram aplicados, sendo os seus resultados discutidos neste documento. Testes experimentais foram realizados usando hardware de nível industrial, nomeadamente o manipulador colaborativo KUKA iiwa7R800, scanner laser, unidades de medição inerciais (IMUs), sensores de força/torque e rastreadores magnéticos
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