Latest Papers

ASME Journal of Mechanisms and Robotics

  • Stable Inverse Dynamics for Feedforward Control of Nonminimum-Phase Underactuated Systems
    on January 25, 2023 at 12:00 am

    AbstractAn enhanced inverse dynamics approach is here presented for feedforward control of underactuated multibody systems, such as mechanisms or robots where the number of independent actuators is smaller than the number of degrees of freedom. The method exploits the concept of partitioning the independent coordinates into actuated and unactuated ones (through a QR-decomposition) and of linearly combined output, to obtain the internal dynamics of the nonminimum-phase system and then to stabilize it through proper output redefinition. Then, the exact algebraic model of the actuated sub-system is inverted, leading to the desired control forces with just minor approximations and no need for pre-actuation. The effectiveness of the proposed approach is assessed by three numerical test cases, by comparing it with some meaningful benchmarks taken from the literature. Finally, experimental verification through an underactuated robotic arm with two degrees of freedom is performed.

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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