References of "Hichri, Bassem 50024991"
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See detailA survey: The usage of Augmented Reality in Industry
Gallala, Abir UL; Hichri, Bassem UL; Plapper, Peter UL

in Robotix-Academy Conference for Industrial Robotics 2018 (2018, November 01)

Human-Robot-Interaction technologies in industry 4.0 and modern manufacturing are more and more growing. Using off-line robot programming methods such as Augmented Reality (AR) could gain time and money ... [more ▼]

Human-Robot-Interaction technologies in industry 4.0 and modern manufacturing are more and more growing. Using off-line robot programming methods such as Augmented Reality (AR) could gain time and money as well as improve programming and repair tasks. This paper is a study of the use of AR in smart factories. [less ▲]

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See detailCILAP-Architecture for Simultaneous Position- and Force-Control in Constrained Manufacturing Tasks
Klecker, Sophie UL; Hichri, Bassem UL; Plapper, Peter UL

in ICINCO 2018 Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics Volume 2 (2018, July)

This paper presents a parallel control concept for automated constrained manufacturing tasks, i.e. for simultaneous position- and force-control of industrial robotic manipulators. The manipulator’s ... [more ▼]

This paper presents a parallel control concept for automated constrained manufacturing tasks, i.e. for simultaneous position- and force-control of industrial robotic manipulators. The manipulator’s interaction with its environment results in a constrained non-linear switched system. In combination with internal and external uncertainties and in the presence of friction, the stable system performance is impaired. The aim is to mimic a human worker’s behaviour encoded as lists of successive desired positions and forces obtained from the records of a human performing the considered task operating the lightweight robot arm in gravity compensation mode. The suggested parallel control concept combines a model-free position- and a model-free torque-controller. These separate controllers combine conventional PID- and PI-control with adaptive neuro-inspired algorithms. The latter use concepts of a reward-like incentive, a learning system and an actuator-inhibitor-interplay. The elements Conventional controller, Incentive, Learning system and Actuator-Preventer interaction form the CILAP-concept. The main contribution of this work is a biologically inspired parallel control architecture for simultaneous position- and force-control of continuous in contrast to discrete manufacturing tasks without having recourse to visual inputs. The proposed control-method is validated on a surface finishing process-simulation. It is shown that it outperforms a conventional combination of PID- and PI-controllers. [less ▲]

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See detailLearning-While Controlling RBF-NN for Robot Dynamics Approximation in Neuro-Inspired Control of Switched Nonlinear Systems
Klecker, Sophie UL; Hichri, Bassem UL; Plapper, Peter UL

in Artificial Neural Networks and Machine Learning; ICANN 2018 part 3 (2018)

Radial Basis Function-Neural Networks are well-established function approximators. This paper presents an adaptive Gaussian RBF-NN with an extended learning-while controlling behaviour. The weights ... [more ▼]

Radial Basis Function-Neural Networks are well-established function approximators. This paper presents an adaptive Gaussian RBF-NN with an extended learning-while controlling behaviour. The weights, function centres and widths are updated online based on a sliding mode control element. In this way, the need for fixing parameters a priori is overcome and the network is able to adapt to dynamically changing systems. The aim of this work is to present an extended adaptive neuro-controller for trajectory tracking of serial robots with unknown dynamics. The adaptive RBF-NN is used to approximate the unknown robot manipulator dynamics-function. It is combined with a conventional controller and a bio-inpsired extension for the control of a robot in the presence of switching constraints and discontinuous inputs. Its learned goal-directed output results from the complementary action of an actuator, A, and a prventer, P. The trigger is an incentive, I, based on the weighted perception of the enviornment. The concept is validated through simulations and implementation on a KUKA LWR4-robot. [less ▲]

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See detailNeuro-Inspired Reward-Based Tracking Control for Robotic Manipulators with Unknown Dynamics
Klecker, Sophie UL; Hichri, Bassem UL; Plapper, Peter UL

in Proceedings of the 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE) (2017, December)

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in ... [more ▼]

Tracking control for robotic manipulators is required for numerous automation tasks in manufacturing engineering. For this purpose, model-free PD-controllers are largely implemented by default in commercially available robot arms and provide satisfactory performance for simple path following applications. Ever more complex automation tasks however ask for novel intelligent and adaptive tracking control strategies. In surface finishing processes, discontinuous freeform paths as well as changing constraints between the robotic end-effector and its surrounding environment affect the tracking control by undermining the stable system performance. The lacking knowledge of industrial robot dynamic parameters presents an additional challenge for the tracking control algorithms. In this paper the control problem of robotic manipulators with unknown dynamics and varying constraints is addressed. A robust sliding mode controller is combined with an RBF (Radial Basis Function) Neural Network-estimator and an intelligent, biomimetic BELBIC (Brain Emotional Learning-Based Intelligent Control) term to approximate the nonlinear robot dynamics function and achieve a robust and adaptive tracking performance. [less ▲]

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See detailRobotix-Academy Conference for Industrial Robotics (RACIR) 2017
Müller, Rainer; Plapper, Peter UL; Brüls, Olivier et al

Book published by Shaker Verlag - 1st ed (2017)

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See detailRobust BELBIC-Extension for Trajectory Tracking Control
Klecker, Sophie UL; Hichri, Bassem UL; Plapper, Peter UL

in Journal of Mechanics Engineering and Automation (2017), 7(2),

In real-life trajectory tracking applications of robotic manipulators uncertain robot dynamics, external disturbances and switching constraints which cannot be accommodated for by a conventional ... [more ▼]

In real-life trajectory tracking applications of robotic manipulators uncertain robot dynamics, external disturbances and switching constraints which cannot be accommodated for by a conventional controller affect the system performance. We suggested an additional control element combining sliding mode and bio-mimetic, neurologically-inspired BELBIC (brain emotional learning-based intelligent control). The former is invariant to internal and external uncertainties and guarantees robust behavior. The latter is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences and with the goal to maximize a reward function. We proofed the stability and the performance of the suggested control scheme through Lyapunov theory and numerical simulations, respectively. [less ▲]

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See detailPID and Biomimetic Variable Structure Path Tracking Control in Automated Surface Finishing Processes
Klecker, Sophie UL; Plapper, Peter UL; Hichri, Bassem UL

in Robotix-Academy Conference for Industrial Robotics (RACIR) 2017 (2017)

This paper addresses freeform surface following control as one of the main challenges in automating surface finishing processes. Successive changes in constraints between the tool attached to the robotic ... [more ▼]

This paper addresses freeform surface following control as one of the main challenges in automating surface finishing processes. Successive changes in constraints between the tool attached to the robotic manipulator and its surroundings are due to complex workpiece-geometries and result in a switched nonlinear system. The control problem of the latter is addressed by industrial state-of-the-art conventional PID control as well as by biomimetic variable structure control which are both applied to a benchmark path tracking problem as characteristically encountered in surface finishing. [less ▲]

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