Latest Papers

ASME Journal of Mechanisms and Robotics

  • Dynamic Modeling and Simulation of a Hybrid Robot
    by Shen N, Yuan H, Li J, et al. on May 12, 2022 at 12:00 am

    AbstractThe unique structure of hybrid robot makes its dynamic characteristic different from that of the traditional machine tools. Therefore, the dynamic model is crucial to both designing and application of hybrid robot. In this paper, a new type of five-degrees-of-freedom (5DoF) hybrid robot is introduced, and its dynamic model is established. First, the kinematic formulas are derived for all the component, and then, the inertia forces or moments are calculated. Second, the active forces or moments in the joints are assumed as variables and the number of variables is reduced by analyzing joint types. Then, an equation set of 36 equilibrium equations with 38 variables is obtained using D'Alembert's principle. Based on the spatial deformation compatibility analysis of two branches, two supplementary equations are derived to determine the solution of dynamic model of the hybrid robot with redundant constraints in its parallel mechanism. Several cases are studied by comparing with ADAMS simulation. The result shows the good accuracy of the proposed dynamic model, which provides a practical method to calculate the reaction force or moment in any joint at any instant for the hybrid robot and thus facilitates dimensional synthesis, trajectory optimization, and smoothing control.

  • Feasibility Design and Control of a Lower Leg Gait Emulator Utilizing a Mobile 3-Revolute, Prismatic, Revolute Parallel Manipulator
    by Soliman A, Ribeiro GA, Gan D, et al. on May 12, 2022 at 12:00 am

    AbstractDesign and control of lower extremity robotic prostheses are iterative tasks that would greatly benefit from testing platforms that would autonomously replicate realistic gait conditions. This paper presents the design of a novel mobile 3-degree-of-freedom (DOF) parallel manipulator integrated with a mobile base to emulate human gait for lower limb prosthesis evaluation in the sagittal plane. The integrated mobile base provides a wider workspace range of motion along the gait direction and reduces the requirement of the parallel manipulator’s actuators and links. The parallel manipulator design is optimal to generate the defined gait trajectories with both motion and force requirements using commercially available linear actuators. An integrated active force control with proportional integral derivative (PID) control provided more desirable control compared to traditional PID control in terms of error reduction. The novelty of the work includes the methodology of human data-oriented optimal mechanism design and the concept of a mobile parallel robot to extend the translational workspace of the parallel manipulator with substantially reduced actuator requirements, allowing the evaluation of prostheses in instrumented walkways or integrated with instrumented treadmills.

  • Announcing the 2021 Best Paper Award and Honorable Mention
    by Krovi V. on May 12, 2022 at 12:00 am

    Together with the Editorial Board of the Journal of Mechanisms and Robotics (JMR), I am pleased to announce the winner of the journal's 2021 Best Paper Award:P. Reinier Kuppens, Miguel A. Bessa, Just L. Herder, and Jonathan B. Hopkins, 2021, “Compliant Mechanisms That Use Static Balancing to Achieve Dramatically Different States of Stiffness,” ASME J. Mech. Robot., 13(2), p. 021010. https://doi.org/10.1115/1.4049438

Modeling of Industrial Robot Kinematics Using a Hybrid Analytical and Statistical Approach

Abstract

Industrial robots are highly desirable in applications including manufacturing and surgery. However, errors in the modeling of the kinematics of robotic arms limit their positional accuracy in industrial applications. Specifically, analytical kinematic models of the robot arm suffer from errors in coefficient calibrations and the inability to account for effects including gear backlash. However, statistical modeling methods require an extensive amount of points for calibration, which is infeasible in practical industrial environments. Hence, this paper describes, develops, and experimentally validates a hybrid modeling methodology combining both analytical and statistical methods to describe the robot kinematics in an intuitive manner that is easily adaptable for small- and medium-sized industries. By formulating an explicitly described analytical kinematic model as a prior mean distribution of a Gaussian process, the prior distribution can be updated with experimental data using statistical Bayesian Inference, thus enabling more accurate description of the robot kinematics with fewer data points. The hybrid model is demonstrated to outperform an analytical model, a neural network model, and a Gaussian Process Regression model with no prior distribution in predicting both the forward and inverse kinematics of a UR5 and UR10 robot arm. Also, the error propagation of the inverse kinematic solutions is studied. In addition, the testing framework used in this work can be used as a standardized benchmark to evaluate alternative kinematic models.

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