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

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.

An unscented Kalman filter based velocity estimation method for articulated steering vehicle using a novel dynamic model

“…22,23,24 However, the coupling relationship between the steering driving force and tire force may introduce estimation errors and increase calculating complications, 25 which is disadvantageous to the design of the estimator. In addition, the internal forces of the articulated steering system that are usually measured indirectly through multiple sensors 26 might incur a higher cost while developing estimators on such models. Some high-dimensional ASV dynamic models without steering systems 27,28 only serve the purpose of theoretically analysing the dynamics, with no extension to the design of controlling and estimating methods for ASV.…”

“…The drawback of using the simplified model is that these estimation methods will leave the asymmetry of ASV out of consideration. Gao et al 26 used the single rail model to estimate the longitudinal velocity of ASV vehicles, which symmetrically considers the tire force of left and right and may decrease estimation accuracy under large slip or load transfer. Jin et al 30 developed a more detailed nonlinear dynamic model for the articulated electric-driven vehicle, combining the model with the UKF to estimate the longitudinal velocity.…”

Longitudinal Velocity Estimation of Driverless Vehicle by Fusing LiDAR and Inertial Measurement Unit

“…In [21], an extended square-root cubic Kalman filter was proposed to combine GPS and IMU. In [22], motion and constraint models were combined with IMU data to overcome interference from gyroscope drift and disturbances in external acceleration. In [23], a novel Kalman filter is proposed to solve the problem of sensor jitter noise, which further improves the estimation accuracy.…”

Coordination Control of Active Steering and Direct Yaw Control for the Articulated Steering Vehicle

“…Te upper controller estimates the vehicle state and generates the active steering angle and torque, and the lower controller distributes the torque to the motors. Gao et al provide a vehicle-based method to estimate the attitude of underground ASV [18]. For the torque distribution algorithms, generalized inversion [19] and quadratic programming methods [20] are provided.…”