# Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination

arXiv: https://arxiv.org/abs/2609.37666 · HTML: https://mlsanity.com/paper/2609.37666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [A Critical Review of Communications in Multi-Robot Systems](https://mlsanity.com/paper/2206.09484/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Large-Scale Multi-Robot Coverage Path Planning via Local Search](https://mlsanity.com/paper/2312.10797/) (2023)
- [DRespNeT: A UAV Dataset and YOLOv8-DRN Model for Aerial Instance Segmentation of Building Access Points for Post-Earthquake Search-and-Rescue Missions](https://mlsanity.com/paper/2508.16016/) (2025)
- [Heterogeneous Multi-Robot Graph Coverage with Proximity and Movement Constraints](https://mlsanity.com/paper/2412.10083/) (2024)
- [A Framework for Semantics-based Situational Awareness during Mobile Robot Deployments](https://mlsanity.com/paper/2502.13677/) (2025)
- [Strengthening Multi-Robot Systems for SAR: Co-Designing Robotics and Communication Towards 6G](https://mlsanity.com/paper/2504.01940/) (2025)
- [3D Semantic Segmentation for Post-Disaster Assessment](https://mlsanity.com/paper/2512.24593/) (2025)
- [6G Empowering Future Robotics: A Vision for Next-Generation Autonomous Systems](https://mlsanity.com/paper/2602.12246/) (2026)

## Abstract (arXiv)

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.