Calibration of Kinematic Parameters for Two Wheel Differential Mobile Robots by Using Experimental Heading Errors

Abstract

Odometry using incremental wheel encoder sensors provides the relative position of mobile robots. This relative position is fundamental information for pose estimation by various sensors for EKF Localization, Monte Carlo Localization etc. Odometry is also used as unique information for localization of environmental conditions when absolute measurement systems are not available. However, odometry suffers from the accumulation of kinematic modeling errors of the wheel as the robot's travel distance increases. Therefore, systematic odometry errors need to be calibrated. Principal systematic error sources are unequal wheel diameters and uncertainty of the effective wheelbase. The UMBmark method is a practical and useful calibration scheme for systematic odometry errors of two-wheel differential mobile robots. However, the approximation errors of the calibration equations and the coupled effect between the two systematic error sources affect the performance of the kinematic parameter estimation. In this paper, we proposed a new calibration scheme whose calibration equations have less approximation errors. This new scheme uses the orientation errors of the robot's final pose in the test track. This scheme also considers the coupled effect between wheel diameter error and wheelbase error. Numerical simulations and experimental results verified that the proposed scheme accurately estimated the kinematic error parameters and improved the accuracy of odometry calibration significantly.

Supervisory Adaptive Network-Based Fuzzy Inference System (SANFIS) Design for Empirical Test of Mobile Robot

“…It is also recommended the AVR Studio development environment which may be downloaded free of charge from the Atmel company. A similar research of mobile robot control for path can be seen from [16] which is considering the coupled effect between two wheels diameter. Their robot is a big mobile robot, but in this paper, a small mobile robot is considered.…”

Efficient Stereoscopic Video Matching and Map Reconstruction for a Wheeled Mobile Robot

“…Recent works that address this same topic with a different approach are: in [9] a method to acquire range information for mobile robots, a DSP-based range finding system with binocular stereo vision is proposed. In [10] the authors propose a new calibration scheme based on calibration equations that introduce fewer approximation errors to reduce kinematic modelling errors introduced by odometry. In [11] a comparative study of bio-inspired algorithms applied to optimize type-1 and type-2 fuzzy controllers for autonomous mobile robots is presented.…”

“…In [10] the authors propose a new calibration scheme based on calibration equations that introduce fewer approximation errors to reduce kinematic modelling errors introduced by odometry. In [11] a comparative study of bio-inspired algorithms applied to optimize type-1 and type-2 fuzzy controllers for autonomous mobile robots is presented.…”

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“…Therefore, the sensor measurement uncertain boundary can be decided in advance by analyzing the measurement data; on the other hand, process noise comes from the mismatch between the system state space model and the systems' real dynamics, such as the parameters' mismatching. For most underactuated and strongly-coupled nonlinear systems -such as robotic systems [22,23,24] -different operating modes result in a dissimilar dynamic model, even parameter transitions, which means that the system cannot be modelled accurately. Therefore, it is necessary to manually tune the boundary parameters since the priori information of uncertain boundaries are unknown.…”