Wang, Xinhao ， Yang, Lin ， Zhang, Daqiang
2026-03-01 JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2026 50(卷), null(期), (null页)
To address the cleaning challenges faced by robots in the extreme conditions of desert photovoltaic power plants, this study proposes a photovoltaic embodied intelligence (EI) robot based on bionic brain-cerebellum coordination control. First, domain knowledge integration is used for multi-objective task planning and interpretable strategy generation, and a dedicated decision-making brain model (PV-LLM) is constructed for desert PV scenarios. Then, a PV-Cerebellum module is designed through simulation-reality dataset learning, thereby ensuring efficient mapping and robust control from abstract tasks to specific skill library invocations. Finally, the two core skills of the proposed robot, real-time obstacle avoidance navigation and panel alignment cleaning, are validated via simulation tests in the Gazebo simulation environment under desert conditions. Experimental results demonstrate that the proposed robotic system can achieve obstacle-avoidance response times as low as 0.36 s and cleaning coverage rates up to 96.67 %, validating its engineering feasibility. Overall, this work provides a new practical solution for reliable photovoltaic panel cleaning in harsh desert environments.