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    Kinematic analysis of the spherically actuated platform manipulator

    , Article 2007 IEEE International Conference on Robotics and Automation, ICRA'07, Rome, 10 April 2007 through 14 April 2007 ; May , 2007 , Pages 175-180 ; 10504729 (ISSN); 1424406021 (ISBN); 9781424406029 (ISBN) Pendar, H ; Vakil, M ; Fotouhi, R ; Zohoor, H ; Sharif University of Technology
    2007
    Abstract
    New methods for the Inverse and forward kinematic analysis of the novel six Degrees of Freedom (6DOF) parallel manipulator which has only two legs are presented. The actuation of the new mechanism is through two base-mounted spherical actuators. In the inverse pose kinematic, active joint variables are directly calculated with no need for the evaluation of passive joint variables. In the forward pose kinematic, closed form solution adopting a new approach is presented. It is shown that the inverse and forward pose kinematic have sixteen and four different solutions, respectively. Moreover, closed form equations for the rate kinematic analysis are proposed. Finally, two different categories... 

    Neural control of a fully actuated biped robot

    , Article 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006, Kunming, 17 December 2006 through 20 December 2006 ; 2006 , Pages 1299-1304 ; 1424405718 (ISBN); 9781424405718 (ISBN) Sadati, N ; Hamed, K. A ; Sharif University of Technology
    2006
    Abstract
    According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with Central Pattern Generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors...