Latest Papers

ASME Journal of Mechanisms and Robotics

  • Mechanical Characterization of Supernumerary Robotic Tails for Human Balance Augmentation
    on August 31, 2023 at 12:00 am

    AbstractHumans are intrinsically unstable in quiet stance from a rigid body system viewpoint; however, they maintain balance, thanks to neuro-muscular sensory control properties. With increasing levels of balance related incidents in industrial and ageing populations globally each year, the development of assistive mechanisms to augment human balance is paramount. This work investigates the mechanical characteristics of kinematically dissimilar one and two degrees-of-freedom (DoF) supernumerary robotic tails for balance augmentation. Through dynamic simulations and manipulability assessments, the importance of variable coupling inertia in creating a sufficient reaction torque is highlighted. It is shown that two-DoF tails with solely revolute joints are best suited to address the balance augmentation issue. Within the two-DoF options, the characteristics of open versus closed loop tails are investigated, with the ultimate design selection requiring trade-offs between environmental workspace, biomechanical factors, and manufacturing ease to be made.

Design of Position Estimator for Rope Driven Micromanipulator of Surgical Robot Based on Parameter Autonomous Selection Model

Abstract

As the micromanipulator of surgical robots works in a narrow space, it is difficult to install any position sensors at the end, so the position control and position detection cannot be accurately performed. A position estimator based on the parameter autonomous selection model is proposed to estimate the end position indirectly. First, a single joint principle prototype and a position estimator model are established through the 4DOF driving scheme of the micromanipulator and the cable-driven model. Second, the proposed parameter change model is combined with the parameter selection method to form a parameter autonomous selection model. Finally, a position estimator based on the parameter autonomous selection model is established. The experimental results show the maximum estimation error of the position estimator is 0.1928 deg. Compared with other position estimation methods, the position estimator proposed in this paper has higher accuracy and better robustness, which lays a foundation for the full closed-loop control of micromanipulator position.

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