Reshaping the singularities in 3 D.O.F 3-RRR planar parallel manipulator by varying the position of the actuator

Krishnan, Keerthana; Ganesh, M.; Dash, Anjan Kumar

doi:10.1109/rctfc.2016.7893413

Cited by 2 publications

(1 citation statement)

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“…In addition, it is necessary to design different types of haptic devices to meet the requirements of different control systems. Krishnan and Ganesh proposed a RRR three degree-of-freedom (DOF) parallel joystick that uses the motion constraint equation and the dynamic modeling of the motion platform to obtain the overall system dynamics equation of a parallel manipulator [3]. A haptic interface algorithm for a joystick to control a remote, unmanned vehicle was proposed [4].…”

Section: Introductionmentioning

confidence: 99%

Impedance matching control between a human arm and a haptic joystick for long-term

Choi

Lee

et al. 2021

Robotica

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An impedance matching control framework between a human and a haptic joystick for long-term teleoperation is proposed in this research. An impedance model of the human arm is established analyzing the characteristics of human perception, decision, and action. The coefficients of the human arm’s impedance have been identified using a least squares method. The human arm’s impedance matching algorithm generates a corresponding motion vector for the human arm, which is determined by the interaction force measured by a force/torque sensor considering the impedance modeling of the human arm. The impedance control has been adopted for the haptic joystick to match the desired impedance to that of the human arm, which is aimed to minimize the energy consumption of the human arm for long-term teleoperation. By minimizing the fatigue of the operator, the remote control accuracy of the teleoperation can be improved. A PD control with gravity compensation algorithm has been adopted to maintain desired trajectory for the joystick by the operator more conveniently. The effectiveness of matching control has been demonstrated by trajectory following experiments for a mobile robot.

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Section: Introductionmentioning

confidence: 99%