Evidence map›Paper›PMID 35918508›Full record

ArticleScientific reports2022

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot.

Dan Xiang, Hanxi Lin, Jian Ouyang, Dan Huang

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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.

Dan XiangSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.
Hanxi LinSchool of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.
Jian OuyangIndustrial Training Center, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China. ouyangjian@gpnu.edu.cn.
Dan HuangThe School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, Guangdong, China.

Funding

Special projects in universities' key fields of Guangdong Province 2020ZDZX2002the National Natural Science Foundation of China 62103112
6 · The paper itself

Abstract

With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

Identifiers

PMID35918508
PMCPMC9345932

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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.