## 4 Conclusions  
This paper proposes an intelligent collaborative navigation model (BBCN) based on Beidou high-precision navigation and binocular vision recognition to address the specific needs of robot navigation in the complex environment of substations. The model's effectiveness was verified through simulation experiments. The research focuses on the system's global positioning capabilities, local path planning capabilities, and dynamic obstacle avoidance performance, solving the issues of Beidou signal interference and blockage in complex industrial environments, and providing a multi-sensor fusion navigation solution. First, this paper deeply analyzed the complex characteristics of substation environments, including the effects of electromagnetic interference, signal blockage, and dynamic obstacles on navigation performance. A collaborative solution was proposed where Beidou navigation provides global positioning references, combined with binocular vision for local environmental perception. By using the Unscented Kalman Filter (UKF) to fuse Beidou positioning data with vision data, the BBCN model can maintain high positioning accuracy in complex environments. Additionally, a collaborative path planning method was designed, combining global and local paths, and dynamic path adjustment and obstacle avoidance strategies were employed to ensure the robot's navigation safety and efficiency in dynamic environments. Experimental results show that the BBCN model exhibits significant performance advantages in the simulation environment. In terms of global positioning, the positioning error of the BBCN model remained within 5 cm under no-interference conditions. Even under high electromagnetic interference, the error increased only to 6.2 cm, significantly outperforming the single Beidou navigation model and other comparison models. In terms of path planning efficiency, the actual path generated by the BBCN model closely matched the ideal path, with a nearly optimal efficiency factor (close to 1), demonstrating high adaptability in obstacle-dense environments. In dynamic obstacle avoidance, the BBCN model achieved a 95% success rate, reliably handling interference from dynamic obstacles, \begin{table}
\begin{tabular}{c c} \hline
**Module** & **Average Processing Time (ms, mean \(\pm\) std)** \\ \hline Beidou Data Processing and WKF & \(3.5\pm 0.4\) \\ Binocular Image Rectification & \(8.2\pm 0.9\) \\ Disparity and Depth Map Generation & \(15.1\pm 1.5\) \\ YOLOv5 Object Detection & \(22.5\pm 2.1\) \\ DeepLab Semantic Segmentation & \(25.3\pm 2.8\) \\ UKF Fusion and State Update & \(2.1\pm 0.3\) \\ Path Planning (A + RMP) & \(3.3\pm 0.5\) \\ \hline \end{tabular}
\end{table} 
Table 6: Computational cost of key BBCN modules. far exceeding traditional GNSS models and single-vision navigation models. Additionally, the system latency of the BBCN model was controlled within 80 ms, meeting the real-time requirements of industrial scenarios. The innovation of the BBCN model lies in its provision of a targeted solution to the unique challenges of substation environments, representing a significant advancement in the practical application of autonomous robotic systems in the power industry. By successfully addressing the electromagnetic interference and signal blockage issues that have previously limited automation in substations, this research opens up new possibilities for comprehensive intelligent inspection and maintenance operations. The practical applications of this technology extend beyond basic navigation to enable fully autonomous equipment inspection, real-time fault detection, preventive maintenance, and emergency response in high-voltage environments--tasks that traditionally expose human workers to significant safety risks. Economic benefits include reduced labor costs (with potential savings of 40-60% on routine inspection tasks), minimized power outages through more frequent and thorough inspections, extended equipment lifespan through early fault detection, and improved operational safety.