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A Method of AEKF With Maximum Likelihood Estimation for Robot State Estimation | IEEE Conference Publication | IEEE Xplore

A Method of AEKF With Maximum Likelihood Estimation for Robot State Estimation


Abstract:

It is critical for inertial navigation systems (INS) to obtain accurate attitude data, as the accuracy of the inertial guidance system directly affects the accuracy of at...Show More

Abstract:

It is critical for inertial navigation systems (INS) to obtain accurate attitude data, as the accuracy of the inertial guidance system directly affects the accuracy of attitude estimation. Firstly, an AEKF was designed by incorporating a kinetic model of the robot, which provides an adaptive method to adjust the covariance of the measurement noise. Secondly, maximum likelihood estimation is introduced into the filtering algorithm to estimate the noise covariance matrix. Finally, simulations were designed to compare the performance differences between the designed AEKF and other filtering algorithms under different dynamic models. The results of simulations show that the designed AEKF is able to better filter the roll rate of robot in dynamic situations and have better estimation accuracy.
Date of Conference: 26-28 July 2024
Date Added to IEEE Xplore: 18 December 2024
ISBN Information:
Conference Location: Hangzhou, China

Funding Agency:


I. Introduction

Recent years, security robots have emerged in large numbers with the demand for various special operations. Similarly, due to the unique shape of petrochemical pipelines a suitable robot is urgently needed to achieve the detection of welds[1], [2] on them. The petrochemical pipeline inspection robot is prone to detachment or difficulty moving forward due to its need to run on the curved surface of the pipeline. Therefore, it is inevitable to install an inertial measurement unit to determine the current posture and position of the robot, while the error of the IMU will greatly affect the stability of the robot's operation on the pipeline. Therefore, multi-sensor navigation systems using high precision and accuracy filters for state estimation have long been essential. The complexity and diversity of the robot's working environment, coupled with the characteristics of the robot's structure can cause various difficulties in data measurement. In last decades, two more excellent classes of algorithms have been developed for the solution of this problem: the Kalman filter (KF)[3] and the complementary filter (CF)[4], [5].

References

References is not available for this document.