Semantic Path Planning for Heterogeneous Robots from Building Digital Twin Data

This paper presents an innovative approach to semantic path planning for mobile robots by integrating semantic data from building digital twins. Semantic and metric information extracted from the digital twin is used to assign weights to a connectivity graph, allowing for path computation using the A* algorithm. Our method excels at generating robot-specific maps that combine both geometric and semantic data, diverging from traditional static maps. This semantic integration equips robots with diverse navigation skills, enabling them to navigate complex environments within large smart facilities. A key innovation of this work is our path planner, which utilizes semantic data from Building Information Modeling (BIM) databases. This marks a significant advancement in mobile robotic navigation, accommodating robots with varying navigation abilities. The significance of this work lies in the seamless integration of semantic data, enhancing the adaptability and efficiency of mobile robots, regardless of their navigation skills. This coordinated navigation system not only improves safety but also optimizes shared space management for both humans and robots.