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  • 1.
    Iliev, Boyko
    et al.
    Örebro University, Department of Technology.
    Kadmiry, Bourhane
    Örebro University, Department of Technology.
    Palm, Rainer
    Örebro University, Department of Technology.
    Interpretation of human demonstrations using mirror neuron system principles2007In: IEEE 6th international conference on development and learning, ICDL 2007, New York: IEEE , 2007, p. 128-133Conference paper (Refereed)
    Abstract [en]

    In this article we suggest a framework for programming by demonstration of robotic grasping based on principles of the Mirror Neuron System (MNS) model. The approach uses a hand-state representation inspired by neurophysiological models of human grasping. We show that such a representation not only simplifies the grasp recognition but also preserves the essential part of the reaching motion associated with the grasp. We show that if the hand state trajectory of a demonstration can be reconstructed, the robot is able to replicate the grasp. This can be done using motion primitives, derived by fuzzy time-clustering from the demonstrated reach-and grasp motions. To illustrate the approach we show how human demonstrations of cylindrical grasps can be modeled, interpreted and replicated by a robot in this framework.

  • 2.
    Kadmiry, Bourhane
    et al.
    Linköpings universitet, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group.
    Bergsten, Pontus
    Örebro University .
    Driankov, Dimiter
    Linköpings universitet, Tekniska högskolan.
    Autonomous Helicopter Control Using Fuzzy-Gain Scheduling2001In: Proceedings of the IEEE International Conference on Robotic & Automation (ICRA), IEEE conference proceedings , 2001, p. 2980-2985Conference paper (Refereed)
    Abstract [en]

    The work reported in the paper is aimed at achieving aggressive manoeuvrability for an unmanned helicopter APID MK-III by Scandicraft AB in Sweden. The manoeuvrability problem is treated at the level of attitude (pitch, roll, yaw) and the aim is to achieve stabilization of the attitude angles within much larger ranges than currently available. We present a fuzzy gain scheduling control approach based on two different types of Iinearization of the original nonlinear APID MK-III model. The performance of the fuzzy gain scheduled controllers is evaluated in simulation and shows that they are effective means for achieving the desired robust manoeuvrability.

  • 3.
    Kadmiry, Bourhane
    et al.
    Linköpings universitet, Tekniska högskolan.
    Driankov, Dimiter
    Linköpings universitet, Tekniska högskolan.
    A Fuzzy Flight Controller Combining Linguistic and Model-based Fuzzy Control2004In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 146, no 3, p. 313-347Article in journal (Refereed)
    Abstract [en]

    In this paper we address the design of a fuzzy flight controller that achieves stable and robust -aggressive- manoeuvrability for an unmanned helicopter. The fuzzy flight controller proposed consists of a combination of a fuzzy gain scheduler and linguistic (Mamdani-type) controller. The fuzzy gain scheduler is used for stable and robust altitude, roll, pitch, and yaw control. The linguistic controller is used to compute the inputs to the fuzzy gain scheduler, i.e., desired values for roll, pitch, and yaw at given desired altitude and horizontal velocities. The flight controller is obtained and tested in simulation using a realistic nonlinear MIMO model of a real unmanned helicopter platform, the APID-MK

  • 4.
    Kadmiry, Bourhane
    et al.
    Linköpings universitet, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group.
    Driankov, Dimiter
    Linköpings universitet, Tekniska högskolan.
    Autonomous Helicopter Control using Linguistic and Model-Based Fuzzy Control2001In: Proceedings of the IEEE International Symposium on Intelligent Control (CCA/ISIC), 2001, p. 348-352Conference paper (Refereed)
    Abstract [en]

    The paper presents the design of a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. The controller is able of regulating high horizontal velocities via stabilization of the attitude angles within much larger ranges than currently available. We use a novel approach to the design consisting of two steps: 1) a Mamdani-type of a fuzzy rules are used to compute for each desired horizontal velocity the corresponding desired values for the attitude angles and the main rotor collective pitch; and 2) using a nonlinear model of the altitude and attitude dynamics, a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. According to our knowledge this is the first time when a combination of linguistic and model-based fuzzy control is used for the control of a complicated plant such as an autonomous helicopter. The performance of the combined linguistic/model-based controllers is evaluated in simulation and shows that the proposed design method achieves its intended purpose

  • 5.
    Kadmiry, Bourhane
    et al.
    Linköping University, Linköping, Sweden.
    Driankov, Dimiter
    Linköping University, Linköping, Sweden.
    Fuzzy control of an autonomous helicopter2001In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference / [ed] Smith, M.H., Gruver, W.A. & Hall, L.O., New York, USA: IEEE conference proceedings, 2001, p. 2797-2802Conference paper (Refereed)
    Abstract [en]

    This work presents a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. We use a novel approach to the design consisting of two steps: 1) Mamdani-type of fuzzy rules to compute each of the desired horizontal velocity corresponding to the desired values for the attitude angles and the main rotor collective pitch; and 2) a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. The performance of the combined linguistic/model-based controller is evaluated in simulation and shows that the proposed design method achieves its intended purpose

  • 6.
    Kadmiry, Bourhane
    et al.
    Linköping University, Linköping, Sweden.
    Driankov, Dimiter
    Örebro University, School of Science and Technology.
    Takagi-Sugeno fuzzy gain scheduling with sampling-time uncertainties2004In: Proceedings 2004 IEEE International Conference on Fuzzy Systems, New York, USA: IEEE conference proceedings, 2004, Vol. 2, p. 1087-1091Conference paper (Refereed)
    Abstract [en]

    This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a robust controller design method is proposed. The sufficient conditions and the design procedure are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated of a visual-servoing control problem.

  • 7.
    Kadmiry, Bourhane
    et al.
    Linköpings universitet, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group.
    Palm, Rainer
    Driankov, Dimiter
    Linköpings universitet, Tekniska högskolan.
    Autonomous Helicopter Control Using Gradient Descent Optimization Method2001In: Proceedings of the Asian Conference on Robotic & Automation (ACRA), 2001Conference paper (Refereed)
  • 8.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Kadmiry, Bourhane
    Grasp recognition by fuzzy modeling and hidden Markov models2010In: Robot intelligence: an advanced knowledge processing approach / [ed] Honghai Liu, Dongbing Gu, Robert J. Howlett, Yonghuai Liu, New York: Springer , 2010, p. 25-47Chapter in book (Other academic)
    Abstract [en]

    Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. This chapter describes three different methods for grasp recognition for a human hand. A human operator wearing a data glove instructs the robot to perform different grasps. For a number of human grasps the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using time as input parameter and joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are compared with each other. In the first method, the test grasp is compared with model grasps using the difference between the model outputs. The second method deals with qualitative fuzzy models which used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning.

  • 9.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Kadmiry, Bourhane
    Örebro University, School of Science and Technology.
    Recognition of human grasps by time-clustering and fuzzy modeling2009In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 57, no 5, p. 484-495Article in journal (Refereed)
    Abstract [en]

    In this paper we address the problem of recognition of human grasps for five-fingeredrobotic hands and industrial robots in the context of programming-by-demonstration. The robot isinstructed by a human operator wearing a data glove capturing the hand poses. For a number ofhuman grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzyclustering and Takagi-Sugeno (TS) modeling. This so-called time-clustering leads to grasp modelsusing time as input parameter and fingertip positions as outputs. For a sequence of grasps thecontrol system of the robot hand identifies the grasp segments, classifies the grasps andgenerates the sequence of grasps shown before. For this purpose, each grasp is correlated with atraining sequence. By means of a hybrid fuzzy model the demonstrated grasp sequence can bereconstructed.

  • 10.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Kadmiry, Bourhane
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Driankov, Dimiter
    Örebro University, School of Science and Technology.
    Recognition and teaching of robot skills by fuzzy time-modeling2009In: Proceedings of the Joint 2009 international fuzzy systems association world congress and 2009 European society of fuzzy logic and technology conference / [ed] J. P. Carvalho, D. U. Kaymak, J. M. C. Sousa, Linz, Austria: Johannes Kepler university , 2009, p. 7-12Conference paper (Other academic)
    Abstract [en]

    Robot skills are low-level motion and/or grasping capabilities that constitute the basic building blocks from which tasks are built. Teaching and recognition of such skills can be done by Programming-by-Demonstration approach. A human operator demonstrates certain skills while his motions are recorded by a data-capturing device and modeled in our case via fuzzy clustering and Takagi-Sugeno modeling technique. The resulting skill models use the time as input and the operator's actions and reactions as outputs. Given a test skill by the human operator the robot control system recognizes the individual phases of skills and generates the type of skill shown by the operator.

  • 11.
    Skoglund, Alexander
    et al.
    Örebro University, Department of Technology.
    Iliev, Boyko
    Örebro University, Department of Technology.
    Kadmiry, Bourhane
    Örebro University, Department of Technology.
    Palm, Rainer
    Örebro University, Department of Technology.
    Programming by demonstration of pick-and-place tasks for industrial manipulators using task primitives2007In: International symposium on computational intelligence in robotics and automation, CIRA 2007, New York: IEEE , 2007, p. 368-373Conference paper (Refereed)
    Abstract [en]

    This article presents an approach to Programming by Demonstration (PbD) to simplify programming of industrial manipulators. By using a set of task primitives for a known task type, the demonstration is interpreted and a manipulator program is automatically generated. A pick-and-place task is analyzed, based on the velocity profile, and decomposed in task primitives. Task primitives are basic actions of the robot/gripper, which can be executed in a sequence to form a complete a task. For modeling and generation of the demonstrated trajectory, fuzzy time clustering is used, resulting in smooth and accurate motions. To illustrate our approach, we carried out our experiments on a real industrial manipulator.

1 - 11 of 11
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  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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