Latest Papers

ASME Journal of Mechanisms and Robotics

  • Measurement Configuration Optimization and Kinematic Calibration of a Parallel Robot
    by Huang C, Xie F, Liu X, et al. on December 10, 2021 at 12:00 am

    AbstractThis paper presents the kinematic calibration of a four-degrees-of-freedom (4DOF) high-speed parallel robot. In order to improve the calibration effect by decreasing the influence of the unobservable disturbance variables introduced by error measurement, a measurement configuration optimization method is proposed. Configurations are iteratively selected inside the workspace by a searching algorithm, then the selection results are evaluated through an index associated with the condition number of the identification Jacobian matrix; finally, the number of optimized configurations is determined. Since the selection algorithm has been shown to be sensitive to local minima, a meta-heuristic method has been applied to decrease this sensibility. To verify the effectiveness of the algorithm and kinematic calibration, computation validations, pose error estimations, and experiments are performed. The results show that the identification accuracy and calibration effect can be significantly improved by using the optimized configurations.

Markov Chain Monte Carlo Parameter Estimation for Nonzero Slip Models of Wheeled Mobile Robots: A Skid-Steer Case Study

Abstract

An accurate modeling, simulation, and estimation of the wheel-terrain interaction and its effects on a robot movement plays a key role in control and navigation tasks, specially in constantly changing environments. We study the calibration of wheel slip models using Particle Markov Chain Monte Carlo methods to approximate the posterior distributions of their parameters. In contrast to classic identification approaches, considering the parameters as random variables allows to obtain a probability measure of the parameter estimations and subsequently propagate their uncertainty to wheel slip-related variables. Extensive simulation and experimental results showed that the proposed methodology can effectively get reliable posterior approximations from noisy sensor measurements in changing terrains. Validation tests also include the applicability assessment of the proposed methodology by comparing it with the integrated prediction error minimization methodology. Field results presented up to 66% of improvement in the robot motion prediction with the proposed calibration approach.
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