Development of an Autonomous Robotic Cleaning System for Mitigating Dust-Induced Efficiency Loss in Solar Photovoltaic Panels

This paper proposed an autonomous robotic cleaning framework to mitigate dust-induced efficiency loss in solar photovoltaic panels. The research problem was formulated around the mismatch between continuous dust accumulation and conventional fixed or manual maintenance. A low-cost integrated architecture was developed conceptually around PV electrical monitoring, a microcontroller, motorized traversal, a soft dry-cleaning element, safety/position sensors and post-cleaning performance verification. The literature survey shows that dust accumulation can significantly reduce PV performance, that cleaning effectiveness is dependent on environmental conditions, and that robotic dry-cleaning has already demonstrated technical feasibility. The main contribution proposed in this work is therefore not robotic cleaning alone, but the integration of condition-based activation and post-cleaning electrical verification with a compact robotic mechanism. This creates a closed maintenance loop: measure performance → identify significant degradation → clean automatically → verify recovery → continue monitoring. The methodology defines controlled dust-loading experiments, repeated cleaning trials, electrical and environmental measurements, power-loss and recovery calculations, auxiliary-energy accounting, safety checks and economic evaluation. The performance section deliberately provides a measurement framework rather than fabricated prototype results. Once experimental data are collected, the manuscript can quantify the dust threshold at which cleaning becomes beneficial, the optimum robot/brush operating parameters, the net energy benefit and the repeatability of autonomous operation. Compared with prior literature that separately investigates dust characterization, cleaning mechanisms, monitoring or cleaning schedules, the proposed approach aims to combine these functions in one low-cost experimental platform. The system can subsequently be extended with optical dust sensing, IoT dashboards and machine-learning scheduling. Such extensions would move the system from simple automation toward predictive, site-adaptive PV maintenance. In conclusion, the proposed autonomous cleaning system provides a practical research pathway for reducing dust-related PV energy losses while minimizing routine manual intervention and water consumption. The final publication should include the authors' actual prototype dimensions, component specifications, measured data, statistical analysis, photographs and experimental results in place of the indicated measurement fields. These additions will be essential before submitting the work as an original experimental research article.

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