Review of key technologies and development trends of photovoltaic panel cleaning robots

With the rapid development of the global photovoltaic industry, enhancing the operational and maintenance efficiency, as well as the actual power output of solar power plants, has become a key concern for the sector. When photovoltaic modules are heavily covered by various pollutants, their power generation efficiency decreases. Studies have shown that when the dust deposition reaches approximately 10 g/m2, the output power of the modules can drop by roughly 15%–34%, influenced by the characteristics of dust particles and the intensity of light. This significantly limits the overall performance of the photovoltaic system. In this context, photovoltaic panel cleaning robots have emerged as a pivotal intelligent solution. This paper elucidates the scientific principles underlying the impact of contamination on photovoltaic module performance, thereby establishing the rationale for panel cleaning. Focusing on a photovoltaic panel cleaning robot as the primary subject of study, the paper systematically reviews and thoroughly investigates its core technological architecture. The research covers a range of design and structural alternatives for the robot, along with an analysis of the functional configuration of the cleaning unit. Furthermore, the study examines the intelligent technologies that enable the robot to operate autonomously, including environmental perception facilitated by sensors and image recognition algorithms, as well as motion control strategies for path planning and stability management. Finally, the research addresses the system integration level, analyzing how photovoltaic panel cleaning robots can be incorporated into the broader operation and maintenance framework to facilitate remote monitoring and human–machine interaction. Through a systematic analysis of the aforementioned key technologies, this paper argues that while photovoltaic cleaning robots have demonstrated the potential to improve module performance, their intelligence level and adaptability to complex scenarios still have significant shortcomings. Future technological evolution needs to focus on stronger environmental perception, higher autonomous decision-making capabilities, and more sophisticated system coordination mechanisms to support their reliable application in large-scale power plants.