Evidence map›Paper›PMID 39727749›Full record

ArticleBiomimetics (Basel, Switzerland)2024

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs.

Haoze Zhuo, Zhong Yang, Chi Zhang, Nuo Xu, Bayang Xue, Zekun Zhu, Yucheng Xie

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Haoze ZhuoCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0009-0003-4670-5655
Zhong YangCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Chi ZhangCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Nuo XuCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Bayang XueCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Zekun ZhuCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0009-0004-6166-7615
Yucheng XieCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Funding

the Guizhou Provincial Science and Technology Projects Guizhou-Sci-Co-Supp [2020]2Y044the National Natural Science Foundation of China 61473144the Science and Technology Projects of China Southern Power Grid Co. Ltd. 066600KK52170074
6 · The paper itself

Abstract

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Indexed as

biomimeticintelligent robotsSLAMtarget trackingtransmission line inspection

Identifiers

PMID39727749
PMCPMC11673162

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.