Enhancing Autonomus Aerial Monitoring of Large-Scale Photovoltaic Plants Using a Deep Reinforcement Learning Approach for Multi-Aerial Robot Systems
Year: 2025
Type: conference-paper
Abstract: Eficient inspection of large-scale photovoltaic (PV) power plants can be achieved using a multi-aerial robot system powered by Deep Reinforcement Learning (DRL). This study introduces an innovative algorithm for online coverage path planning to optimize the efficiency, scalability, and adaptability of multi-aerial robot systems. By dynamically adapting to real-time environmental changes, the algorithm ensures comprehensive inspection coverage while reducing computational overhead. Simulations conducted in both grid-based and realistic environments demonstrate the algorithm's robustness and its potential to revolutionize inspection tasks in dynamic and complex scenarios.
Source: EU PVSEC
Authors: Aghamohammadi, Amirhossain, Esmailifar, Seyyed Majid, Kolahi, Mohammad, Moradi Sizkouhi, Amirmohammad, Aghaei, Mohammadreza
Language: English
Topic: Power Line Inspection Robots
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106309262
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