Evidence map›Paper›PMID 36298265›Full record

ReviewSensors (Basel, Switzerland)2022

The Use of Terrestrial and Maritime Autonomous Vehicles in Nonintrusive Object Inspection.

Dmytro Mamchur, Janis Peksa, Antons Kolodinskis, Maksims Zigunovs

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
0.9field-weighted citation impact, top 25% 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, 9 citations in OpenAlex.

  1. Review
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

4 authors at 2 institutions in 2 countries.

Dmytro MamchurInformation Technologies Department, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia.
Janis PeksaInstitute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia.ORCID 0000-0003-4125-494X
Antons KolodinskisInformation Technologies Department, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia.
Maksims ZigunovsInformation Technologies Department, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia.
Turība University · LVRiga Technical University · LV

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could be applied for this task. The present study is devoted to a review of the existing and novel technologies and methods of using autonomous self-driving vehicles for nonintrusive object inspection. Both terrestrial and maritime self-driving vehicles, their typical construction, sets of sensors, and software algorithms used for implementing self-driving motion were analyzed. The standard types of sensors used for nonintrusive object inspection in security checks at the control points, which could be successfully implemented at self-driving vehicles, along with typical areas of implementation of such vehicles, were reviewed, analyzed, and classified.

Indexed as

artificial intelligenceclassificationnonintrusive inspectionself-driving vehicle

Identifiers

PMID36298265
PMCPMC9611526
OpenAlexW4306769526

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.