Evidence map›Paper›PMID 42096449›Full record

ArticlePloS one2026

Real environment obstacle circular edge expansion design robot path planning based on ant colony algorithm.

Feng Li, Maoya Yang, Seong-Nam Jo, Young-Chul Kim, Ziang Lyu

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

5 authors.

Feng LiDepartment of Intelligent Manufacturing, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China.ORCID https://orcid.org/0000-0003-0639-7611
Maoya YangDepartment of Mechanical Engineering, Kunsan National University, Gunsan, Jeollabuk, Republic of Korea.
Seong-Nam JoDepartment of Mechanical Engineering, Kunsan National University, Gunsan, Jeollabuk, Republic of Korea.
Young-Chul KimDepartment of Mechanical Engineering, Kunsan National University, Gunsan, Jeollabuk, Republic of Korea.
Ziang LyuDepartment of Mechanical Engineering, Kunsan National University, Gunsan, Jeollabuk, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of artificial intelligence, mobile robot motion control technology is facing the key problem of intelligent path planning. The combination of a grid map and an artificial intelligence algorithm provides an effective solution for path planning. However, the current grid map generation mainly relies on laser and ultrasonic sensors to obtain environmental information for grid map modeling, which is time-consuming and laborious. To address this issue, this paper investigates a method to transform a non-standard real environment map into a standard grid map. The non-standard real environment map was processed, and the size and shape of the map were standardized. The obstacles in the map were designed as a circle; the edge was expanded to add safety distance. The experimental results show that the method can transform real environment photos into standard grid maps suitable for robot path planning, and the path planning and motion control of robots in real environments can be carried out by using this method.

Indexed as

AlgorithmsAntsArtificial IntelligenceRoboticsAnimalsMotion

Identifiers

PMID42096449
PMCPMC13152169

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.