ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Field Robotics
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1606774
This article is part of the Research TopicReal-Time Trajectory Planning for Autonomous Mobile RobotsView all articles
A photovoltaic panel cleaning robot with a lightweight YOLO v8
Provisionally accepted
- College of Mechanical and Electrical Engineering, Tarim University, Aral, Xinjiang Uyghur Region, China
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Cleaning PV (photovoltaic) panels is essential for a PV station, as dirt or dust reduces the effective irradiation of solar energy and weakens the energy efficiency of solar cells, 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 PV 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.
Keywords: Cleaning robot, photovoltaic station management, lightweight YOLO v8, deep learning enhancement, path planning
Received: 06 Apr 2025; Accepted: 24 Jul 2025.
Copyright: © 2025 Luo, Wang, Lei, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jidong Luo, College of Mechanical and Electrical Engineering, Tarim University, Aral, 843300, Xinjiang Uyghur Region, China
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