• DocumentCode
    1607089
  • Title

    Forward and inverse kinematics of a robotic frog

  • Author

    Dasari, A. ; Reddy, Nandyala Sreeramula

  • Author_Institution
    Mech. Eng. Dept., Nat. Inst. of Technol. Durgapur, Durgapur, India
  • fYear
    2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Among all the kinds of locomotion adapted by the living organisms in nature, jumping has proven itself as one of the most efficient ways of traversal through rough terrain. Frogs are the efficient and stable organisms which use jumping as their primary means of locomotion. For any legged robot to take a perfect jump, taking off perfectly is one of the most important aspects. So, the trajectory of the center of gravity (CG) of the robot is of the utmost importance. For this, the motion and coordination of the legs should be very precise for which, we have to carry out the forward and inverse kinematics of the robot. In this paper, we used Denavit-Hartenberg (DH) Parameters to carry out the forward kinematics. Jacobian and Artificial Neural Networks (ANN) methods are used to compute the inverse kinematics of the robot and a comparison of performance of these two methods is presented.
  • Keywords
    Jacobian matrices; legged locomotion; neural nets; robot kinematics; trajectory control; ANN methods; CG trajectory; DH parameters; Denavit-Hartenberg parameters; Jacobian methods; artificial neural networks methods; center of gravity trajectory; forward kinematics; legged robot; legs coordination; living organisms; robot inverse kinematics; robotic frog; Artificial neural networks; Jacobian matrices; Joints; Kinematics; Legged locomotion; Robot kinematics; Artificial Neural Networks; DH Parameters; Forward Kinematics; Inverse Kinematics; Jacobian; Robotic Frog;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on
  • Conference_Location
    Kharagpur
  • Print_ISBN
    978-1-4673-4367-1
  • Type

    conf

  • DOI
    10.1109/IHCI.2012.6481850
  • Filename
    6481850