A Wall-Mounted Robot Arm Equipped With a 4-DOF Yaw-Pitch-Yaw-Pitch Counterbalance Mechanism

Min, Jae Kyung; Kim, Do Won; Song, Jae-Bok

doi:10.1109/lra.2020.2975731

Cited by 18 publications

(4 citation statements)

References 11 publications

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“…Moreover, each order mode is usually obtained by the product of the modal coordinates and the modal function, and nally, the consecutive several order modes are added to obtain the vibration amount at any point on the entire exible arm. Literature [5] has demonstrated that the use of the second-order mode can accurately re ect the vibration o set of the exible body. is research method has also been well veri ed experimentally and theoretically; however, this research method has certain de ciencies and cannot be applied to exible manipulators with variable cross-sectional areas [6].…”

Section: Introductionmentioning

confidence: 99%

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

2022

Journal of Robotics

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In order to improve the parameter control effect of the double-joint manipulator, this paper combines the RBF neural network to control the parameters of the double-joint manipulator and the command filtering backstep impedance control method based on the RBF neural network is effectively applied to the multijoint manipulator. Moreover, this paper introduces the filter error compensation mechanism into the controller design to eliminate the influence caused by the filter error. Finally, the effectiveness and superiority of the command filtering backstep impedance control scheme of the multijoint manipulator adaptive neural network designed in this paper is verified by simulation experiments. The experimental research results verify that the parameter-tunable RBF neural network control method facing the dual-joint manipulator has a certain effect on the parameter control process of the dual-joint manipulator and can effectively improve the motion accuracy of the dual-joint manipulator.

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Section: Introductionmentioning

confidence: 99%

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

2022

Journal of Robotics

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“…However, this method will not only increase the dead-weight of the manipulator, but also increase the driving torque required for joint acceleration; Springs: when the manipulator is in different poses, the gravity torque can be partly or completely offset through the interconversion between the elastic potential energy and the gravitational potential energy. This kind of gravity compensation method includes direct application of torsional spring, 6 compression spring with pulley block, 7 ZFL spring used with cable, [8][9][10] and slightly complex mechanism used with cam 11 or slider 12 ; Auxiliary actuators: additional actuators for balancing are connected to the linkage 13 or directly to the end-platform. 14 The main limitation of the above methods is that they can only balance the fixed load (usually the dead-weight of the manipulator), or auxiliary links are needed so that the structure is slightly more complex.…”

Section: Introductionmentioning

confidence: 99%

“…Springs: when the manipulator is in different poses, the gravity torque can be partly or completely offset through the interconversion between the elastic potential energy and the gravitational potential energy. This kind of gravity compensation method includes direct application of torsional spring, 6 compression spring with pulley block, 7 ZFL spring used with cable, 8–10 and slightly complex mechanism used with cam 11 or slider 12 ;…”

Section: Introductionmentioning

confidence: 99%

Adaptive gravity-balancing mechanism using adjustable spring for a 6-DoF serial cooperative manipulator

Liu,

Chen,

Gao

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Cumbersome high-power joint actuators are not suitable for lightweight cooperative manipulators, which require flexibility and high energy efficiency while maintaining moderate payload capacity. Thus, gravity compensation is proposed in this work to drive cooperative manipulators by small joint actuators. Most existing gravity compensation methods are focused on fixed load (i.e. dead-weight), while in this study, a variable-payload balanced 6-DoF (degrees of freedom) serial manipulator is realized by adaptive gravity-balancing mechanism. Since joint actuators can be small enough to withstand only joint friction and inertia, this manipulator is inherently safe for man-robot interaction. The adaptive gravity-balancing mechanism is composed of ZFL (zero-free length) springs with adjustable endpoints, which are actively adjusted by cable after payload changes. Three advantages of the proposed manipulator include: Firstly, balancing all joints affected by gravity by only one additional balancing motor; Secondly, the balancing mechanism can actively adapt to variable payloads; Finally, the adjustment of the balancing mechanism is independent of the manipulator’s pose. Through simulation, these three advantages have been verified, which provides a new scheme for the design of safe-interaction and energy-saving cooperative manipulators.

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“…Kim et al [5] developed a novel balance mechanism for a 6-DOF robotic arm, which uses a spring and slider crank within a double-parallelogram mechanism based on bevel gear units. Min et al [6] developed a passive counterbalance mechanism (CBM) based on springs and wires to compensate for gravitational effects arising from the roll and pitch joint motions of a wall-mounted robotic arm. Huang et al [7] developed a robotic arm with a spherical joint module, a rotary joint module, and a hybrid serial-parallel electric drive system.…”

Section: Introductionmentioning

confidence: 99%

Mechanism Development and Pulse-Width Modulation Advanced Controller Design of Low Torque Manipulator

Wu,

Lee,

Yan

et al. 2023

IEEE Access

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In most conventional robotic arms, the motors are installed on the rotating shafts within the arm, which means that the shape and size of the motors must be considered in the design of the arm, while the weight of the motors increases the torque on the supporting joints generated during arm movements. This paper presents a dual-axis robotic arm with the motors installed on the base of the device to overcome these issues. Movement around the first axis is driven by a gear, while that of the second axis is driven by a belt. Trajectory tracking is controlled by an adaptive sliding mode controller (ASMC). In simulations and experiments, the proposed robotic arm outperformed arms using PID control and sliding mode control (SMC) in terms of tracking accuracy and stability.INDEX TERMS Robotic arm, low torque, adaptive sliding mode control, tracking error.

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A Wall-Mounted Robot Arm Equipped With a 4-DOF Yaw-Pitch-Yaw-Pitch Counterbalance Mechanism

Cited by 18 publications

References 11 publications

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

Adaptive gravity-balancing mechanism using adjustable spring for a 6-DoF serial cooperative manipulator

Mechanism Development and Pulse-Width Modulation Advanced Controller Design of Low Torque Manipulator

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