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Updated: Sep 9, 2025
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
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AI-Integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based
Indra Kishor1, Udit Mamodiya2, Vathsala Patil3
1Department of CSE, Poornima Institute of Engineering & Technology, Jaipur, 302022, Rajasthan, India.
Scientific Reports
|September 1, 2025
Summary
This study introduces an AI-powered robotic system for autonomous solar panel cleaning and fault detection. The intelligent system enhances energy output and reduces maintenance costs in dusty environments.
Area of Science:
- Renewable Energy Systems
- Artificial Intelligence
- Robotics
Background:
- Solar photovoltaic (PV) systems face performance degradation in dusty, high-temperature regions due to soiling and heating.
- Manual maintenance is often delayed, inefficient, and labor-intensive, impacting overall energy yield.
Purpose of the Study:
- To develop and evaluate an AI-integrated autonomous robotic system for real-time monitoring, fault detection, and intelligent cleaning of solar panels.
- To enhance solar panel performance, energy efficiency, and operational resilience.
Main Methods:
- A hybrid system combining Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) for fault detection and Deep Q-Network (DQN) Reinforcement Learning for robotic cleaning.
- Utilized thermal and LiDAR-equipped drones for fault detection and ground robots for cleaning, supported by Edge AI analytics on Jetson Nano and Raspberry Pi 4B.
- Implemented MQTT-based IoT communication for real-time data exchange.
Main Results:
- Achieved 91.3% average cleaning efficiency, reducing dust density and restoring up to 31.2% energy output.
- CNN-LSTM fault detection reached 92.3% accuracy; RL cleaning reduced energy and water consumption by 34.9%.
- Edge inference latency averaged 47.2 ms, outperforming cloud processing by 63%; confirmed strong correlation (r=0.87) between dust and thermal anomalies.
Conclusions:
- The AI-driven autonomous robotic system offers a resilient and intelligent solution for proactive solar panel maintenance.
- Leveraging AI, robotics, and edge computing enhances energy efficiency, reduces manual labor, and supports climate-resilient smart solar infrastructure.
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