Lehrgebiet: Theoretische Informatik und künstliche Intelligenz
Büro: 01.214
Labor: 04.105
Telefon: +49 208 88254-806
E-Mail:
Ioannis Iossifidis studierte Physik (Schwerpunkt: theoretische Teilchenphysik) an der Universität Dortmund und promovierte 2006 an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum.
Am Institut für Neuroinformatik leitete Prof. Dr. Iossifidis die Arbeitsgruppe Autonome Robotik und nahm mit seiner Forschungsgruppe erfolgreich an zahlreichen, vom BmBF und der EU, geförderten Forschungsprojekten aus dem Bereich der künstlichen Intelligenz teil. Seit dem 1. Oktober 2010 arbeitet er an der HRW am Institut Informatik und hält den Lehrstuhl für Theoretische Informatik – Künstliche Intelligenz.
Prof. Dr. Ioannis Iossifidis entwickelt seit über 20 Jahren biologisch inspirierte anthropomorphe, autonome Robotersysteme, die zugleich Teil und Ergebnis seiner Forschung im Bereich der rechnergestützten Neurowissenschaften sind. In diesem Rahmen entwickelte er Modelle zur Informationsverarbeitung im menschlichen Gehirn und wendete diese auf technische Systeme an.
Ausgewiesene Schwerpunkte seiner wissenschaftlichen Arbeit der letzten Jahre sind die Modellierung menschlicher Armbewegungen, der Entwurf von sogenannten «Simulierten Realitäten» zur Simulation und Evaluation der Interaktionen zwischen Mensch, Maschine und Umwelt sowie die Entwicklung von kortikalen exoprothetischen Komponenten. Entwicklung der Theorie und Anwendung von Algorithmen des maschinellen Lernens auf Basis tiefer neuronaler Architekturen bilden das Querschnittsthema seiner Forschung.
Ioannis Iossifidis’ Forschung wurde u.a. mit Fördermitteln im Rahmen großer Förderprojekte des BmBF (NEUROS, MORPHA, LOKI, DESIRE, Bernstein Fokus: Neuronale Grundlagen des Lernens etc.), der DFG («Motor‐parietal cortical neuroprosthesis with somatosensory feedback for restoring hand and arm functions in tetraplegic patients») und der EU (Neural Dynamics – EU (STREP), EUCogII, EUCogIII ) honoriert und gehört zu den Gewinnern der Leitmarktwettbewerbe Gesundheit.NRW und IKT.NRW 2019.
ARBEITS- UND FORSCHUNGSSCHWERPUNKTE
- Computational Neuroscience
- Brain Computer Interfaces
- Entwicklung kortikaler exoprothetischer Komponenten
- Theorie neuronaler Netze
- Modellierung menschlicher Armbewegungen
- Simulierte Realität
WISSENSCHAFTLICHE EINRICHTUNGEN
- Labor mit Verlinkung
- ???
- ???
LEHRVERANSTALTUNGEN
- ???
- ???
- ???
PROJEKTE
- Projekt mit Verlinkung
- ???
- ???
WISSENSCHAFTLICHE MITARBEITER*INNEN
Felix Grün
Büro: 02.216 (Campus Bottrop)
Marie Schmidt
Büro: 02.216 (Campus Bottrop)
Aline Xavier Fidencio
Gastwissenschaftlerin
Muhammad Ayaz Hussain
Doktorand
Tim Sziburis
Doktorand
Farhad Rahmat
studentische Hilfskraft
AUSGEWÄHLTE PUBLIKATIONEN
-
2010
3.Reimann, Hendrik; Iossifidis, Ioannis; Schöner, Gregor
End-effector obstacle avoidance using multiple dynamic variables Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Schlagwörter: arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model, obstacle avoidance
@inproceedings{Reimannd,
title = {End-effector obstacle avoidance using multiple dynamic variables},
author = {Hendrik Reimann and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {The avoidance of obstacles is a crucial part of the generation of behavior for autonomos robotic agents. A standard method to produce trajectories to a given target that avoids a number of possibly mobile obstacles is the potential field approach introduced by Khatib, where an artificial potential field is constructed around target and obstacles, with the target acting as a global minimum and the obstacles as local maxima, the gradient of which is used to determine the (artificial) force acting on the robot at any moment. While the potential field approach has been used extensively for vehicle motion in a plane, applications for robotic manipulators suffer from a high level of complexity due to the formulation of constraints as forces necessitating the inclusion of dynamic properties of the manipulator into the system. We pursue a different solution to the problem of manipulator obstacle avoidance based on the dynamic approach to robotics, which states that all behavioral constraints for the generation of movement should be formulated as attractors or repellors of a dynamical systems. The problem of behavior design is thus separated from the control problem of how to realize the designed behavior, bringing the advantage of simplicity in the formulation of the former.},
keywords = {arm movement model, Autonomous robotics, behavior generation, Dynamical systems, movement model, obstacle avoidance},
pubstate = {published},
tppubtype = {inproceedings}
}
The avoidance of obstacles is a crucial part of the generation of behavior for autonomos robotic agents. A standard method to produce trajectories to a given target that avoids a number of possibly mobile obstacles is the potential field approach introduced by Khatib, where an artificial potential field is constructed around target and obstacles, with the target acting as a global minimum and the obstacles as local maxima, the gradient of which is used to determine the (artificial) force acting on the robot at any moment. While the potential field approach has been used extensively for vehicle motion in a plane, applications for robotic manipulators suffer from a high level of complexity due to the formulation of constraints as forces necessitating the inclusion of dynamic properties of the manipulator into the system. We pursue a different solution to the problem of manipulator obstacle avoidance based on the dynamic approach to robotics, which states that all behavioral constraints for the generation of movement should be formulated as attractors or repellors of a dynamical systems. The problem of behavior design is thus separated from the control problem of how to realize the designed behavior, bringing the advantage of simplicity in the formulation of the former.2009
2.Iossifidis, Ioannis; Schöner, Gregor
Reaching while avoiding obstacles: a neuronally inspired attractor dynamics approach Proceedings Article
In: Bernstein Conference on Computational Neuroscience (BCCN 2009), 2009.
Links | BibTeX | Schlagwörter: anthropomorphic arm, central nervous system, collision avoidance, Dynamical systems, manipulator dynamics, obstacle avoidance, redundant manipulators, redundant robot arm
@inproceedings{Iossifidis2009,
title = {Reaching while avoiding obstacles: a neuronally inspired attractor dynamics approach},
author = {Ioannis Iossifidis and Gregor Schöner},
doi = {10.3389/conf.neuro.10.2009.14.007},
year = {2009},
date = {2009-01-01},
booktitle = {Bernstein Conference on Computational Neuroscience (BCCN 2009)},
keywords = {anthropomorphic arm, central nervous system, collision avoidance, Dynamical systems, manipulator dynamics, obstacle avoidance, redundant manipulators, redundant robot arm},
pubstate = {published},
tppubtype = {inproceedings}
}
2004
1.Iossifidis, Ioannis; Schöner, Gregor; Schoner, Gregor
Autonomous reaching and obstacle avoidance with the anthropomorphic arm of a robotic assistant using the attractor dynamics approach Proceedings Article
In: Proc. IEEE International Conference on Robotics and Automation ICRA '04, S. 4295––4300 Vol.5, 2004, ISSN: 1050-4729.
Abstract | Links | BibTeX | Schlagwörter: anthropomorphic arm, attractor dynamics, autonomous reaching, collision avoidance, end effector shift, end effectors, man machine interaction, manipulator dynamics, obstacle avoidance, robotic assistant, time varying environment, time-varying systems
@inproceedings{Iossifidis2004b,
title = {Autonomous reaching and obstacle avoidance with the anthropomorphic arm of a robotic assistant using the attractor dynamics approach},
author = {Ioannis Iossifidis and Gregor Schöner and Gregor Schoner},
doi = {10.1109/ROBOT.2004.1302393},
issn = {1050-4729},
year = {2004},
date = {2004-01-01},
booktitle = {Proc. IEEE International Conference on Robotics and Automation ICRA '04},
volume = {5},
pages = {4295----4300 Vol.5},
abstract = {To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics approach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan make it possible to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that emerge as long as the arm is far from obstacles make the movement goals of the robotic assistant predictable for the human operator, improving man-machine interaction.},
keywords = {anthropomorphic arm, attractor dynamics, autonomous reaching, collision avoidance, end effector shift, end effectors, man machine interaction, manipulator dynamics, obstacle avoidance, robotic assistant, time varying environment, time-varying systems},
pubstate = {published},
tppubtype = {inproceedings}
}
To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics approach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan make it possible to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that emerge as long as the arm is far from obstacles make the movement goals of the robotic assistant predictable for the human operator, improving man-machine interaction.