## 1 Introduction  
With the rapid development of intelligent robotics technology and the Beidou Satellite Navigation System (BDS), autonomous mobile robots based on high-precision positioning have been widely used in industrial inspection, smart logistics, autonomous driving, and other fields. Especially in the power industry, the demand for automated substation inspection has been increasing [1, 2]. Robots replacing humans in high-voltage equipment inspection, fault detection, and maintenance has become a trend. Through high-precision navigation and positioning technology, mobile robots can efficiently complete autonomous path planning and navigation tasks, greatly improving operational efficiency and reducing labor costs. The Beidou Navigation Satellite System (BDS), particularly its third generation (BDS-3), has been providing global Positioning, Navigation, and Timing (PNT) services since 2020 [3]. Unlike other Global Navigation Satellite Systems (GNSSs), such as GPS (USA), GLONASS (Russia), and Galileo (EU), BDS utilizes a unique hybrid constellation of Geostationary Earth Orbit (GEO), Inclined Geosynchronous Orbit (IGSO), and Medium Earth Orbit (MEO) satellites. This architecture provides enhanced coverage and accuracy in the Asia-Pacific region, making it particularly suitable for applications within China [4]. While our proposed fusion framework is applicable to other GNSSs, this study focuses on BDS due to its regional performance advantages and its strategic importance for critical national infrastructure like the power grid. However, the unique characteristics of substation environments present severe challenges for the application of high-precision navigation systems [5; 6; 7]. Substation environments pose severe challenges for high-precision navigation. The dense arrangement of high-voltage equipment generates strong electromagnetic interference that degrades satellite signal quality [8; 9; 10]. Concurrently, metal structures and buildings cause significant signal obstruction and multipath effects. The complex layout of these environments, with narrow passages and dynamic obstacles, further complicates autonomous navigation, rendering global positioning from Beidou alone insufficient for safe and reliable operation [11; 12]. To address these challenges, researchers have proposed multi-sensor fusion techniques, combining Beidou high-precision positioning, visual recognition, LiDAR, inertial navigation, and other methods to enable efficient navigation and obstacle avoidance for robots in complex environments [13]. Among them, binocular vision technology, as an important environmental sensing tool, can obtain real-time depth information based on the principle of disparity. Combined with artificial intelligence object-detection algorithms (such as YOLO, SSD, DeepLab), it can achieve dynamic object recognition, obstacle detection, and local path planning [14]. Moreover, the development of visual SLAM technology has further promoted the application of binocular vision in robot navigation. Its advantage lies in the ability to complete environmental modeling and positioning tasks based solely on visual data, without the need for external positioning signals [15]. However, the intelligent collaboration between current vision technology and Beidou high-precision navigation systems is still not mature. Specifically, when dealing with electromagnetic interference or Beidou signal obstruction, how to achieve intelligent fusion and switching of vision and Beidou data remains a challenge that needs to be solved [16; 17]. This paper proposes an intelligent collaborative technology based on Beidou high-precision navigation and binocular artificial intelligence visual recognition, aiming to achieve stable autonomous navigation and obstacle avoidance for robots in the presence of electromagnetic interference and signal blockage in complex substation environments.