Prof. Dr. rer. nat. Ioannis Iossifidis

AUSGEWÄHLTE PUBLIKATIONEN

• anthropomorphic robot arm arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems force sensing grippers haptic interface human anatomy human motor behavior human perception inverse kinematics Machine Learning man machine interaction man-machine systems man-machine-interaction manipulators movement model object recognition operators gaze operators gesture pervasive design robot manipulator control robotic assistant robots gripper sensory channels

  150 Einträge « ‹ 5 von 15 › »

  2019

  110.

  Saif-ur-Rehman, Muhammad; Lienkämper, Robin; Dyck, Susanne; Rayana, A; Parpaley, Y; Wllner, J; Liu, Charles; Lee, Brian; Kellis, Spencer; Manahan-Vaughn, D; Güntürkün, O; Andersen, Richard; Iossifidis, Ioannis; Glasmachers, Tobias; Klaes, Christian

  Universal SpikeDeeptector Sonstige

  2019.

  Abstract | BibTeX | Schlagwörter: BCI, CNN, Machine Learning, Spike Detection, Spike Sorting

  @misc{ur-reimann2019a,
  
  title = {Universal SpikeDeeptector},
  
  author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Susanne Dyck and A Rayana and Y Parpaley and J Wllner and Charles Liu and Brian Lee and Spencer Kellis and D Manahan-Vaughn and O Güntürkün and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},
  
  year  = {2019},
  
  date = {2019-01-01},
  
  urldate = {2019-01-01},
  
  publisher = {SfN 2019},
  
  abstract = {State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.},
  
  keywords = {BCI, CNN, Machine Learning, Spike Detection, Spike Sorting},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.

  Schließen

  2018

  109.

  Hussain, Muhammad Ayaz; Klaes, Christian; Iossifidis, Ioannis

  Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Proceedings Article

  In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model

  @inproceedings{bccn18,
  
  title = {Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title},
  
  author = {Muhammad Ayaz Hussain and Christian Klaes and Ioannis Iossifidis},
  
  year  = {2018},
  
  date = {2018-01-01},
  
  urldate = {2018-01-01},
  
  booktitle = {BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018},
  
  publisher = {BCCN},
  
  abstract = {To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror

  Schließen

  2017

  108.

  Iossifidis, Ioannis; Hussain, Muhammad Ayaz; Klaes, Christian

  Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017a,
  
  title = {Temporal stabilized arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Muhammad Ayaz Hussain and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.},
  
  keywords = {Autonomous robotics, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  The generation of discrete movement with distinct and stable time courses characterizes each human movement and reflect the need to perform catching and interception tasks and for timed action sequences, incorporating dynamically changing environmental constraints. Several lines of evidence suggest neuronal mechanism for the initiation of movements i.e. in the supplementary motor area (SMA) and the premotor cortex and for movement planning mechanism generating velocity profiles satisfying time constraints. In order to meet the requirements of on-line evolving trajectories we propose a model, based on dynamical systems which describes goal directed trajectories in humans and generates trajectories for redundant anthropomorphic robotic arms The current study aim to evaluate the temporal characteristics of primary motor and posterior parietal cortex in patients with tetraplegia by using inception task implemented in virtual reality. The participants will be implanted with two 96-channel intracortical microelectrode arrays in the Primary Motor and Post Parietal Cortex. In the training phase the participants will be confronted with the observation of a robotic arm intercepting the bob of a pendulum at the lowest point of it's trajectory (maximum velocity) - the end effector reaches at the same time as the bob of the pendulum the lowest point of the trajectory performing a perfectly timed movement. The arm is positioned perpendicular to the oscillation plane exactly at the hight of the interception point to generate a one dimensional trajectory to the target. The time to contact between the robot's end effector and the bob of the pendulum is maintained constant and during the different sessions the distance between end effector and the point of interception is gradually increased. In order to catch up and to reach in time, either velocity formation or initiation time of the movement have to be changed. Both effects will be investigated independently. For the decoding of movement-related information we introduce a framework exploiting a deep learning approach with a convolutional neural networks.

  Schließen

  107.

  Klaes, Christian; Iossifidis, Ioannis

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Konferenz

  SfN Meeting 2017, 2017.

  BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @conference{nokey,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Christian Klaes and Ioannis Iossifidis},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  booktitle = {SfN Meeting 2017},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {conference}
  
  }
  
  

  Schließen

  106.

  Iossifidis, Ioannis; Klaes, Christian

  Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia Sonstige

  2017.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic

  @misc{Iossifidis2017b,
  
  title = {Low dimensional representation of human arm movement for efficient neuroprosthetic control by individuals with tetraplegia},
  
  author = {Ioannis Iossifidis and Christian Klaes},
  
  year  = {2017},
  
  date = {2017-01-01},
  
  urldate = {2017-01-01},
  
  publisher = {SfN 2017},
  
  abstract = {Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.},
  
  keywords = {Autonomous robotics, BCI, Dynamical systems, movement model, neuroprosthetic},
  
  pubstate = {published},
  
  tppubtype = {misc}
  
  }
  
  

  Schließen

  Over the last decades the generation mechanism and the representation of goal- directed movements has been a topic of intensive neurophysiological research. The investigation in the motor, premotor, and parietal areas led to the discovery that the direction of hand's movement in space was encoded by populations of neurons in these areas together with many other movement parameters. These distributions of population activation reflect how movements are prepared ahead of movement initiation, as revealed by activity induced by cues that precede the imperative signal (Georgopoulos, 1991). Inspired by those findings a model based on dynamical systems was proposed both, to model goal directed trajectories in humans and to generate trajectories for redundant anthropomorphic robotic arms. The analysis of the attractor dynamics based on the qualitative comparison with measurements of resulting trajectories taken from arm movement experiments with humans (Grimme u. a., 2012) created a framework able to reproduce and to generate naturalistic human like arm trajectories (Iossifidis und Rano, 2013; Iossifidis, Schöner u. a., 2006). The main idea of the methodology is to choose low-dimensional, behavioral va- riables of the goal task can be represented as attractor states of those variables. The movement is generated through a dynamical system with attractors and repellers on the behavioral space, at the goal and constraint positions respectively. When the motion of the robot evolves according to the dynamics of these systems, the behavioral variables will be stabilized at their attractors. Movement is represented by the polar coordinates $phi$,$theta$ of the movement direction (heading direction) and the angular frequency $ømega$ of a hopf oscillator, generating the velocity profile of the arm movement. Therefore, the system dynamics will be expressed in terms of these variables. The target and each obstacle induce vector fields over these variables in a way that states where the hand is moving closer to the target are attractive, while states where it is moving towards an obstacle are repellant. Contributions from different sources are weighted by different factors, e.g. in the vicinity of an obstacle, the contribution from that obstacle must dominate the behavior to guarantee constraint satisfaction (collision prevention). Based on three parameters the presented framework is able to generate temporal stabilized (timed) discrete movements, dealing with disturbances and maintaining an approximately constant movement time. In the current study we will implant two 96-channel intracortical microelectrode arrays in the primary motor and the posterior parietal cortex (PPC) of an individual with tetraplegia. In the training phase the parameters of the dynamical systems will be tuned and optimized by machine learning algorithms. Rather controlling directly the arm movement and adjusting continuously parameters, the patient adjust by his or hers thoughts the three parameters of the dynamics, which remain almost constant during the movement. Only when the motion plan is changing the parameters have to be readjusted. The target directed trajectory evolves from the attractor solution of the dynamical systems equations, which means that the trajectory is generated while the system is in a stable stationary state, a fixed-point attractor. The increase of the degree of assistance lowers the cognitive load of the patient and enables the acknowledgement of the desired task without frustration. In addition we aim to replace the robotic manipulator by an exoskeleton for the upper body which will enable the patients to move his or hers own limbs, which would complete the development of a real neuroprosthetic device for every day use.

  Schließen

  2014

  105.

  Iossifidis, Ioannis

  Simulated Framework for the Development and Evaluation of Redundant Robotic Systems Proceedings Article

  In: International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014, 2014.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, man machine interaction, simulated reality

  @inproceedings{Iossifidis2014a,
  
  title = {Simulated Framework for the Development and Evaluation of Redundant Robotic Systems},
  
  author = {Ioannis Iossifidis},
  
  year  = {2014},
  
  date = {2014-01-01},
  
  booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014},
  
  abstract = {In the current work we present a simulated environment for the development and evaluation of multi redundant open chain manipulators. The framework is implemented in Matlab and provides solutions for the kinematics and dynamics of an arbitrary open chain manipulator. For a anthropomorphic trunk-shoulder-arm configura- tion with in total nine degree of freedoms, a closed form solution of the inverse kinematics problem is derived. The attractor dynamics approach to motion generation was evaluated within this framework and the results are verified on the real anthropomorphic robotic assistant Cora.},
  
  keywords = {Autonomous robotics, man machine interaction, simulated reality},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  In the current work we present a simulated environment for the development and evaluation of multi redundant open chain manipulators. The framework is implemented in Matlab and provides solutions for the kinematics and dynamics of an arbitrary open chain manipulator. For a anthropomorphic trunk-shoulder-arm configura- tion with in total nine degree of freedoms, a closed form solution of the inverse kinematics problem is derived. The attractor dynamics approach to motion generation was evaluated within this framework and the results are verified on the real anthropomorphic robotic assistant Cora.

  Schließen

  104.

  Iossifidis, Ioannis

  Development of a Haptic Interface for Safe Human Robot Collaboration Proceedings Article

  In: International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014, 2014.

  Abstract | BibTeX | Schlagwörter: Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction

  @inproceedings{Iossifidis2014b,
  
  title = {Development of a Haptic Interface for Safe Human Robot Collaboration},
  
  author = {Ioannis Iossifidis},
  
  year  = {2014},
  
  date = {2014-01-01},
  
  booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2014, PECCS2014},
  
  abstract = {In the context of the increasing number of collaborative workplaces in industrial environments, where humans and robots sharing the same workplace, safety and intuitive interaction is a prerequisite. This means, that the robot can (1) have contact with his own body and the surrounding objects, (2) the motion of the robot can be corrected online by the human user just by touching his artificial skin or (3) interrupt the action in dangerous situations. In the current work we introduce a haptic interface (artificial skin) which is utilized to cover the arms of an anthropomorphic robotic assistant. The touched induced input of the artificial skin is interpreted and fed into the motor control algorithm to generate the desired motion and to avoid harm for human and machine.},
  
  keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  In the context of the increasing number of collaborative workplaces in industrial environments, where humans and robots sharing the same workplace, safety and intuitive interaction is a prerequisite. This means, that the robot can (1) have contact with his own body and the surrounding objects, (2) the motion of the robot can be corrected online by the human user just by touching his artificial skin or (3) interrupt the action in dangerous situations. In the current work we introduce a haptic interface (artificial skin) which is utilized to cover the arms of an anthropomorphic robotic assistant. The touched induced input of the artificial skin is interpreted and fed into the motor control algorithm to generate the desired motion and to avoid harm for human and machine.

  Schließen

  103.

  Iossifidis, Ioannis

  Development of a Haptic Interface for Safe Human Robot Collaboration Proceedings Article

  In: International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014, 2014.

  BibTeX | Schlagwörter: Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction

  @inproceedings{Iossifidis2014c,
  
  title = {Development of a Haptic Interface for Safe Human Robot Collaboration},
  
  author = {Ioannis Iossifidis},
  
  year  = {2014},
  
  date = {2014-01-01},
  
  booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},
  
  keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  102.

  Iossifidis, Ioannis

  Simulated Framework for the Development and Evaluation of Redundant Robotic Systems Proceedings Article

  In: International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014, 2014.

  BibTeX | Schlagwörter: Autonomous robotics, man machine interaction, simulated reality

  @inproceedings{Iossifidis2014ab,
  
  title = {Simulated Framework for the Development and Evaluation of Redundant Robotic Systems},
  
  author = {Ioannis Iossifidis},
  
  year  = {2014},
  
  date = {2014-01-01},
  
  booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},
  
  keywords = {Autonomous robotics, man machine interaction, simulated reality},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  101.

  Iossifidis, Ioannis

  Development of a Haptic Interface for Safe Human Robot Collaboration Proceedings Article

  In: International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014, 2014.

  BibTeX | Schlagwörter: Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction

  @inproceedings{Iossifidis2014e,
  
  title = {Development of a Haptic Interface for Safe Human Robot Collaboration},
  
  author = {Ioannis Iossifidis},
  
  year  = {2014},
  
  date = {2014-01-01},
  
  booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2014},
  
  keywords = {Autonomous robotics, direct physical interaction, haptic interface, human robot collaboration, man machine interaction},
  
  pubstate = {published},
  
  tppubtype = {inproceedings}
  
  }
  
  

  Schließen

  150 Einträge « ‹ 5 von 15 › »