Latest Papers

ASME Journal of Mechanisms and Robotics

  • Dual Quaternions Representation of Lagrange's Dynamic Equations
    on June 5, 2023 at 12:00 am

    AbstractThis paper introduces for the first time, the Lagrange's dynamic equations in dual number quaternion form. Additionally, Rayleigh's dissipation function in dual quaternion form is introduced here allowing for the accounting of dissipative (non-conservative) forces such as motion through a viscous fluid, friction, and spring damping force. As an example, dual quaternions are used here to derive the Lagrange dynamic equations of a robot manipulator.

  • Geometric Error Calibration of XYZ -3RPS Hybrid Kinematic Machine via Binocular Vision
    on June 5, 2023 at 12:00 am

    AbstractTo improve the motion accuracy of an XYZ-3RPS hybrid kinematic machine (HKM), a geometric error calibration method via binocular vision measurement is studied. First, to separately calibrate the series kinematic mechanisms (SKMs) and parallel kinematic mechanisms (PKMs), the geometric error identification equations (GEIEs) of the XYZ SKM and 3RPS PKM are derived, respectively. By analyzing the different influence principles of the geometric errors on the position and attitude of the 3RPS PKM, a constraint function is added to the GEIE of the PKM to improve the calculation accuracy. Moreover, the geometric error compensation strategy is based on the structural characteristics of the XYZ-3RPS HKM. In addition, based on the principle of binocular vision measurement, two calibration plates, called dynamic and static calibration plates, are designed as markers to define the coordinate systems, enabling the acquisition of full positions and attitudes. Furthermore, a marker transformation method and an in-situ adjustment method are designed to determine the positions and attitudes of the HKM required for calibration such that the marker is always at the center of the field of view of the camera to improve measurement accuracy. Finally, the effectiveness of the calibration method is verified through prototype experiments.

  • Design of Position Estimator for Rope Driven Micromanipulator of Surgical Robot Based on Parameter Autonomous Selection Model
    on June 5, 2023 at 12:00 am

    AbstractAs 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.

  • Kinematic Modeling and Open-Loop Control of a Twisted String Actuator-Driven Soft Robotic Manipulator
    on June 5, 2023 at 12:00 am

    AbstractRealizing high-performance soft robots is challenging because many existing soft or compliant actuators exhibit limitations like fabrication complexity, high power requirement, slow actuation, and low force generation. Due to their high-force output and power efficiency, compactness, and simplicity in fabrication, twisted string actuators (TSAs) have exhibited strong potential in mechatronic and robotic applications. However, they have had limited uses in soft robotics. Consequently, modeling and control of TSA-driven soft robots have not been sufficiently studied. This article presents the first study on the modeling and control of a TSA-driven soft robotic manipulator. A physics-based model was developed to predict the manipulator’s kinematic motion. An inverse model was derived to realize open-loop control. Models that describe the behavior of TSAs were utilized in a novel way to develop the proposed kinematic and inverse models of the soft robot. The proposed modeling and control approaches were experimentally verified to be effective. For example, the modeling and control errors of the bending angle were 1.60 deg (3.11%) and 2.11 deg (3.68%), respectively.

Characterization, Design, and Experimentation of a Fabric-Based Wearable Joint Sensing Device on Human Elbow


The use of conductive fabrics (CFs) in the design of wearables for joint sensing has recently received much interest in a wide range of applications such as robotics, rehabilitation, personal wellness, and sports. However, one key limitation in the existing measurement approach is that the user’s anthropometric information is required to relate the joint parameters to the CF sensor strain reading. This paper seeks to address this limitation by evaluating a new wearable device concept that comprises a CF strain–voltage sensor embedded as part of an inverted slider-crank (ISC) mechanism for joint extension sensing. This benefits from not requiring anthropometric information from the user to relate the joint parameters to the fabric strain readings, as opposed to an existing design. We first characterize the electromechanical property of a commercially available CF. Second, we formulate the joint sensing device’s geometric synthesis procedure as a constrained revolute joint system, where the CF is designed and introduced as an RPR chain to obtain an ISC linkage. Lastly, we designed our wearable sensing device and validated against an ISC linkage fixture representing an elbow joint and an actual healthy human subject’s left elbow. The ISC linkage fixture experimental setup shows that our designed joint sensing device can track the elbow extension motion of 140 deg with a maximum error of 7.66%. The results from our human subject’s left elbow show that it can track the elbow flexion–extension at various angular motion, with error ranges between 8.24 deg and 12.86 deg, and have provided us with an acceptable average Spearman’s coefficient values rs at 0.95.
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