Evidence map›Paper›PMID 35684804›Full record

ReviewSensors (Basel, Switzerland)2022

A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systems.

Masoumeh Rahimi, Haochen Liu, Isidro Durazo Cardenas, Andrew Starr, Amanda Hall, Robert Anderson

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. 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
2.2field-weighted citation impact, top 13% of its field
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, 22 citations in OpenAlex.

  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 at 2 institutions in 1 country.

Masoumeh RahimiSchool of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.ORCID 0000-0001-5395-9280
Haochen LiuSchool of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.
Isidro Durazo CardenasSchool of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.ORCID 0000-0002-3586-041X
Andrew StarrSchool of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK.ORCID 0000-0001-9046-560X
Amanda HallNetwork Rail, Milton Keynes MK9 1EN, UK.
Robert AndersonNetwork Rail, Milton Keynes MK9 1EN, UK.
Cranfield University · GBNetwork Rail · GB

Funding

Network Rail 881574
6 · The paper itself

Abstract

Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle's position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.

Indexed as

RailroadsRoboticsHumansReproducibility of Resultsautonomous systemslocalisationrailway maintenancesensor fusion

Identifiers

PMID35684804
PMCPMC9185565
OpenAlexW4281812588

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.