Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation.This research was partially funded by the Ministry of Economy, Industry and Competitiveness in the project with reference RTC-2014-3070-5. In addition, the work has been partially funded by the project Strategic Action in Robotics, Computer Vision and Automation financed by University Carlos III of Madrid

Exercise adaptation and learning for ankle rehabilitation using parallel robot

[EN] This work presents a methodology for learning and adaptation of a 3-PRS parallel robot skills for ankle rehabilitation. Passive exercises have been designed to train dorsi/plantar flexion, inversion/eversion ankle movements. During exercises, forces may be high because patient cannot follow the desired trajectory. While small errors in the desired trajectory can cause important deviations in the desired forces, pure position control is inappropriate for tasks that require physical contact with the environment. The proposed algorithm takes as input the reference trajectory and force profile, then adapts the robot movement by introducing small offsets to the reference trajectory so that the resulting forces exerted by the patient match the reference profile. The learning procedure is based on Dynamic Movement Primitives (DMPs).[ES] Este trabajo presenta una metodología para aprendizaje y adaptación de trayectorias ejecutadas por un robots paralelo 3-PRS para tareas de rehabilitación en tobillos. Se han diseñado ejercicios pasivos de entrenamiento para movimientos de flexión dorsal/plantar y inversión/eversión. Durante los ejercicios se pueden producir fuerzas elevadas debido a que el paciente no puede seguir la trayectoria planeada. Pequeños desplazamientos de la trayectoria pueden causar importantes desviaciones en las fuerzas deseadas. Por tanto, el control puro de posición es inapropiado para tareas que requieren contacto físico con el entorno. El algoritmo propuesto toma como entrada una trayectoria y un perfil de fuerzas, que el robot ejecuta adaptándola a las necesidades del paciente. El algoritmo introduce una pequeña desviación de la trayectoria patrón cuando la fuerza que ejerce el paciente difiere con el perfil de fuerzas esperado. El método de aprendizaje y adaptación se basa en Primitivas de Movimiento Dinámico (DMPs).Abu Dakka, FJMD. (2015). Exercise adaptation and learning for ankle rehabilitation using parallel robot. http://hdl.handle.net/10251/61804Archivo delegad