Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles

Zhu, Qingyuan; Xiao, Chengliang; Hu, Huosheng; Liu, Yuanhui; Wu, Jinjin

doi:10.3390/s18010212

Cited by 10 publications

(11 citation statements)

References 24 publications

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“…Proof of Theorem 1. Given X = X 0 , the objective function becomes function of F. After omitting the terms of the objective function that are independent with F, we can rewrite the JNSRA problem in (10) as follows.…”

Section: Computing Resource Allocation Problemmentioning

confidence: 99%

See 1 more Smart Citation

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Pham

Nguyen

et al. 2019

Symmetry

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The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

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Section: Computing Resource Allocation Problemmentioning

confidence: 99%

“…Accordingly, many applications for autonomous vehicles have emerged, and can be classified into safety applications (e.g., localization and navigation, obstacle detection, accident avoidance, remote-sensing, etc.) [7][8][9][10] and non-safety applications (e.g., media sharing, infotainment, file transfer, gaming, etc.) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Pham

Nguyen

et al. 2019

Symmetry

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“…The roll angle of agricultural machine can be measured in multiple ways [5][6][7][8][9], and the commonly used method is based on inertial sensors. Inertial sensors generally include accelerometers and gyroscopes [10][11][12][13] or inclination sensors [14,15]. For instance, an external acceleration inclination angle Kalman filter algorithm was proposed for agricultural machinery [1].…”

Section: Introductionmentioning

confidence: 99%

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

et al. 2020

Sensors

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Accurately obtaining roll angles is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. Given the demand for the acquisition of agricultural equipment roll angles, a roll angle monitoring model based on Kalman filtering and multi-source information fusion was established by using the MTi-300 AHRS inertial sensor (INS) and XW-GI 5630 BeiDou Navigation Satellite System (BDS), which were installed on agricultural equipment. Data of the INS and BDS were fused by MATLAB; then, Kalman filter was used to optimize the data, and the state equation and measurement equation of the integrated system were established. Then, an integrated monitoring terminal man-machine interactive interface was designed on MATLAB GUI, and a roll angle monitoring system based on the INS and BDS was designed and applied into field experiments. The mean absolute error of the integrated monitoring system based on multi-source information fusion during field experiments was 0.72 • , which was smaller compared with the mean absolute errors of roll angle monitored by the INS and BDS independently (0.78 • and 0.75 • , respectively). Thus, the roll angle integrated model improves monitoring precision and underlies future research on navigation and independent operation of agricultural equipment.

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“…With the advances in micro-electro-mechanical systems (MEMS), inertial measurement units (IMUs), in which a gyroscope and an accelerometer are included, have been widely used for the estimation of attitude and positioning. Zhu et al [24] presented a novel system for estimating the attitude and measuring the stability of articulated heavy vehicles. A set of IMU sensors formed the hardware foundation of the system, and several sensors were adopted by considering the special structure of articulated vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the target vehicles that were studied in previous papers were four-wheeled commercial vehicles [29], which can be considered to be single rigid bodies. However, different from the target vehicles in the abovementioned studies, UAVs contain two parts, which are connected to each other with center articulation [24]. Although some methods have been applied to articulated vehicles, the dynamics, the kinematic model, and the defects in IMU sensors have not been considered.…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Jin

2019

Sensors

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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.

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Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles

Cited by 10 publications

References 24 publications

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

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

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