Evidence map›Paper›PMID 34945378›Full record

ArticleMicromachines2021

Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother.

Jiangtao Zheng, Sihai Li, Shiming Liu, Bofan Guan, Dong Wei, Qiangwen Fu

Abstract read
In one paragraph

Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jiangtao ZhengSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Sihai LiSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Shiming LiuSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Bofan GuanSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Dong WeiSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Qiangwen FuSchool of Automation, Northwestern Polytechnical University, Xi'an 710072, China.ORCID 0000-0001-6895-7923

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

Indexed as

closing path optimal estimation model (CPOEM)kalman filtermicro-electromechanical inertial measurement unit (MIMU)Rauch-Tung-Striebel (RTS) smoothershearer positioning

Identifiers

PMID34945378
PMCPMC8707076

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.