A Model-Based Method for Estimating the Attitude of Underground Articulated Vehicles.

Lulu Gao,Fei Ma,Chun Jin

doi:10.3390/s19235245

Lulu Gao, Fei Ma + Show 1 more

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https://doi.org/10.3390/s19235245

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Abstract

This paper presents a novel model-based method for estimating the attitude of underground articulated vehicles (UAV). We selected the Load–Haul–Dump (LHD) vehicle as our application object, as it is a typical UAV. First, we established the involved models of the LHD vehicle, including a kinematic model, the linear and angular constraints of a center articulation model, and a dynamic four degrees-of-freedom (DOF) yaw model. Second, we designed a Kalman filter (KF) to integrate the kinematic and constraint models with the data from an inertial measurement unit (IMU), overcoming gyroscope drift and disturbances in external acceleration. In addition, we designed another KF to estimate the yaw based on the dynamic yaw model. The accuracy of the estimations was further enhanced by data fusion. Then, the proposed method was validated by a simulation and a field test under different dynamic conditions. The errors in the estimation of roll, pitch, and yaw were 3.8%, 2.4%, and 4.2%, respectively, in the field test. The estimated longitudinal acceleration was used to obtain the velocity of the LHD vehicle; the error was found to be 1.2%. A comparison of these results to those of other methods showed that the proposed method has high precision. The proposed model-based method will greatly benefit the location, navigation, and control of UAVs without any artificial infrastructure in a global positioning system (GPS)-free environment.

Highlights

With the increase in depth of underground mines, a hazardous environment and safety problems are becoming increasingly prominent
The percentage was obtained with the maximum root mean square (RMS) and the largest values of corresponding angles
The maximum error in the estimations appeared in C9, which was caused by the slip of the LHD vehicle during steering

Summary

Introduction

With the increase in depth of underground mines, a hazardous environment and safety problems are becoming increasingly prominent. Autonomous vehicles (AVs) are urgently needed to ensure that underground mines are safe and efficient [1,2]. As a typical and important kind of underground vehicle, underground articulated vehicles (UAVs) are widely used in underground mines due to their advantages of a higher maneuverability and efficiency [3]. The automation of their precise control and navigation requires related information on the target vehicle [4,5,6], which includes the attitude, velocities, and even accelerations relative to different directions [7]. What needs to be emphasized is that, a global positioning system (GPS) can provide the related information with high accuracy, obstruction of the signal can cause the system to be invalid in some urban areas and tunnels, which is an especially serious problem in underground mines [9]. As efficient vehicles for Sensors 2019, 19, 5245; doi:10.3390/s19235245 www.mdpi.com/journal/sensors

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