Evidence map›Paper›PMID 41245199›Full record

ArticleFrontiers in robotics and AI2025

A photovoltaic panel cleaning robot with a lightweight YOLO v8.

Jidong Luo, Guoyi Wang, Yanjiao Lei, Dong Wang, Yayong Chen, Hongzhou Zhang

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. 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

6 authors.

Jidong LuoCollege of Mechanical and Electrical Engineering, Tarim University, Alar, China.
Guoyi WangCollege of Mechanical and Electrical Engineering, Tarim University, Alar, China.
Yanjiao LeiCollege of Mechanical and Electrical Engineering, Tarim University, Alar, China.
Dong WangCollege of Mechanical and Electrical Engineering, Tarim University, Alar, China.
Yayong ChenXinjiang Production and Construction Corps (XPCC) Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, Alar, China.
Hongzhou ZhangCollege of Mechanical and Electrical Engineering, Tarim University, Alar, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cleaning PV (photovoltaic) panels is essential for a PV station, as dirt or dust reduces the effective irradiation of solar energy and weakens the efficiency of converting solar energy into free electrons. The inconsistent (cleaning efficacy) and unsafe (summarized voltage and current) manual method is a challenge for a PV station. Therefore, this paper develops a cleaning robot with PV detection, path planning, and action control. Firstly, a lightweight Mobile-VIT (Mobile Vision Transformer) model with a Self-Attention mechanism was used to improve YOLOv8 (You Only Look Once v8), resulting in an accuracy of 91.08% and a processing speed of 215 fps (frames per second). Secondly, an A* and a DWA (Dynamic Window Approach) path planning algorithm were improved. The simulation result shows that the time consumption decreased from 1.19 to 0.66 s and the Turn Number decreased from 23 to 10 p (places). Finally, the robot was evaluated and calibrated in both indoor and outdoor environments. The results showed that the algorithm can successfully clean PV arrays without manual control, with the rate increasing by 23% after its implementation. This study supports the maintenance of PV stations and serves as a reference for technical applications of deep learning, computer vision, and robot navigation.

Indexed as

cleaning robotdeep learning enhancementlightweight YOLO v8path planningphotovoltaic station management

Identifiers

PMID41245199
PMCPMC12615241

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.