Telemanipuláció intelligens térben = Telemanipulation in Intelligent Space

A jelen OTKA pályázat személyes kapcsolaton alapuló nemzetközi együttműködéseknek nyújtott anyagi hátteret. Az eredmények bemutatására két nemzetközi demonstráció készült. A budapesti 3D virtuális kiterjesztett valóságot az interneten keresztül összekapcsoltuk egy tokiói és egy narviki intelligens térrel. Az első esetben egy mobil robotot vezéreltünk. Az operátor a japán labor 3D animált másában Budapesten irányított egy animált robotot. Az animált mobil robot mozgását leíró információt az interneten keresztül közvetítette a tokiói intelligens térhez, amely ennek megfelelően irányította a valóságos mobil robotot. A japán mobil robot mozgását az ottani intelligens tér elosztott intelligenciájú érzékelők segítségével érzékelte, és a mozgásállapotra vonatkozó adatokat visszaküldte a magyar virtuális 3D térbe, ahol a virtuális mobil robot a japán mobil robottal kvázi szinkronban mozgott. A második demonstrációban egy mozgáskövető adatruhát viselő norvég operátor kezének mozgatásával mutatta meg, hogy milyen pályát kellene követnie az ipari robotnak. Egy budapesti ipari robot a gesztusokkal kiadott és interneten közvetített parancsokat végrehajtotta. Ez a robotok programozásának teljesen új paradigmája, amely nem igényel robotprogramozási szakértelmet. Megoldott tudományos kihívások: Az internet okozta idő késleltetés kompenzálása, Robot mozgása közben fellépő súrlódás kompenzálása, Ember-gép interakció alternatív kognitív kommunikációs csatornán keresztül. | This OTKA project provided financial background for international cooperations based on personal contacts. Two international demonstrations were made to present achievements of the project. The 3D augmented reality, located in Budapest, was connected to intelligent spaces in Tokyo and Narvik. In the first demonstration a mobile robot was controlled. The Hungarian operator entered into the 3D animated copy of the real laboratory located in Tokyo to operate an animated robot. Its motion data was sent to Tokyo to the Intelligent Space via internet. The Intelligent Space controlled a real mobile robot according to the data received from Budapest. It monitored the movement of the real robot with distributed intelligent sensors. The motion data of the real robot was sent back to the Hungarian 3D virtual space, where the movement of the real and animated robot was synchronised. In the second demonstration the Norwegian operator, wearing a motion caption data suite, showed – with his hand - the desired motion of the industrial robot. The industrial robot in Budapest received the motion data via internet and carried out the commands, that were originally given by gestures in Norway. This is a new paradigm of robot programming, since it does not require qualified knowledge of robot programming. Solved challenges: Compensation of the effects caused by time delay and frictions of robot movement, human-machine interaction via alternative cognitive communication channel

Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions

Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approac