Constructing Visibility Maps of Optimal Positions for Robotic Inspection in Ultra-High Voltage Centers

摘要

Visibility maps are crucial for autonomous robotic applications such as exploration, path planning, obstacle avoidance, and multi-robot coordination. In the context of electrical transmission infrastructure, automated robotic inspection enhances proactive maintenance, enabling early detection of wear, damage, or faults, thereby improving safety, extending component lifespan, and optimizing maintenance schedules.In this work, we propose an algorithm to compute optimal visibility locations, enabling a mobile robot to acquire RGB and thermal images for fault detection. Using LiDAR scans, we construct a global 3D map composed of ground structures (represented as a Grid Map) and overground structures (modeled with an OctoMap for efficient ray-casting). We apply clustering techniques to identify 3D bounding boxes for electrical components and define suitable source and target points for visibility assessment. By employing a weighted visibility scoring approach, we determine the ground positions that offer the best visibility of each component while ensuring minimal occlusions and adherence to viewing constraints.The proposed method enables a robot to autonomously navigate to these optimal viewpoints, improving inspection efficiency. By integrating visibility regions across multiple components, the inspection process is further optimized, reducing overall inspection time. Our algorithm has been successfully deployed and tested at an Ultra-High Voltage Center (UHVC) in Greece, demonstrating its effectiveness in real-world conditions.