Edge based semantic maps improve coordinated robot coverage in 6G networks
Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination
RoboticsArtificial Intelligence
Summary
Robots working together in rescue missions often struggle to share information efficiently and plan better routes based on their abilities. The authors created a way for aerial robots to make simple maps that describe the terrain, which ground robots can understand and use to choose where they can safely go. By only sharing updates to the map instead of the full maps all the time, the robot team communicates faster and covers more ground effectively without sending impossible assignments.
What this means in practice
- •For robotics operators: Coordinate aerial and ground robot teams to improve search area coverage with fewer communication delays and fewer unreachable assignments.
- •For telecom network engineers: Implement semantic and capability-aware communication protocols for AI-native 6G systems to optimize bandwidth use in robotic coordination.
Authors
Abdulqader Dhafer, Qi Wang, Zhou Daniel Hao
Abstract
Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground robots. However, conventional coverage methods typically do not translate perceived terrain into platform-specific reachability, while continuous image exchange imposes a high communication cost. We propose an edge-centric, semantic-aware coverage planning framework that integrates aerial terrain perception, robot-specific traversability reasoning, and payload-efficient semantic state sharing. Aerial observations are converted into compact semantic grid maps, enabling reachability-constrained area decomposition and capability-aware coverage paths that assign only regions admitted by each robot's capability profile. The resulting perception-sharing-planning loop feeds semantic corrections into traversability reasoning and replanning, forming an application-level mechanism motivated by AI-enabled goal-oriented communication envisioned for AI-native 6G networks. For the high-update case, transmitting semantic corrections reduces the application payload by a factor of approximately $82$ relative to periodic full-map sharing. Across matched benchmark scenarios, the proposed method achieved $91.5\%$ coverage with no capability-infeasible allocations, compared with $78.8\%$ coverage and a $21.5\%$ capability-infeasible allocation rate for LS-MCPP. Semantic corrections update the shared planning state without requiring repeated transmission of the complete map.