Substation Equipment-Terrain Map Structure Modeling and Chassis Dynamic Control Based on Graphormer
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Abstract
To address the limitations of insufficient dynamic correlation modeling between complex terrain and substation equipment layouts and the low efficiency of real-time multimodal sensor fusion, this paper proposes a collaborative framework for graph structure modeling and chassis dynamic control based on Graphormer. The proposed approach enables dynamic representation learning of equipment– terrain topological relationships and multimodal data-driven stability optimization, providing enhanced environmental perception and autonomous navigation capabilities for intelligent inspection systems operating in complex electromagnetic power infrastructures. An equipment–terrain graph is constructed by incorporating centrality encoding, spatial encoding, and edge feature encoding to capture global topological dependencies. A cross-modal attention mechanism is integrated with a reinforcement learning strategy to achieve multimodal sensor fusion and adaptive chassis stability control. Experimental results demonstrate that the proposed method achieves a positioning error standard deviation of only 1.48 cm and obtains the optimal node-to-ground-truth matching performance. The average path success rate across ten simulation scenarios reaches 91.6%, outperforming mainstream approaches, while the fluctuation energy of pitch and roll angles within the 4–8 Hz frequency band is reduced to 14.0% and 15.5%, respectively. The proposed framework provides an effective graph neural network solution for autonomous substation inspection robots and offers technical support for reliable perception, navigation, and intelligent operation in modern electromagnetic energy systems.
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