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.
Figures & tables
Fig. 1: Framework overview. Aerial or satellite imagery is converted into a semantic map and combined with robot capabilities and initial positions for area partitioning and coverage planning. The semantic map and path assignments are distributed to aerial and ground robots, where onboard functions support local event detection and replanning. Isaac Sim provides the evaluation testbed.
Fig. 2: Semantic perception pipeline from aerial imagery to a grid-based semantic map. Pixel masks are aggregated by dominant class into a discrete semantic grid for capability-aware multi-robot planning.
ID
Class
Description
0
Water
Flooded areas, standing water, and ponds
1
Building No Damage
Intact structures with no visible damage
2
Building Medium Damage
Visible structural or roof damage; structure remains standing
3
Building Major Damage
Partial collapse or severe structural failure
4
Building Total Destruction
Rubble or fully collapsed structures
5
Vehicle
Cars and trucks
TABLE I: Terrain Classes Used for Semantic Segmentation
Fig. 3: Multi-robot semantic coverage in Isaac Sim. Snapshot of a simulated disaster environment showing aerial robots (green) and quadruped ground robots (blue). Red cubes denote discretized semantic grid cells used to encode robot-specific traversability and coverage maps. Magnified insets show one aerial and one ground robot during execution.
Planner
Cov. (%) ↑
Infeas. alloc. (%) ↓
Proposed
91.5
0.0
DARP [ 19 ]
74.2
21.0
Modified boustrophedon [ 17 ]
71.9
22.6
LS-MCPP [ 22 ]
78.8
21.5
MFC rooted-tree cover [ 23 ]
79.1
21.5
TABLE II: Mean planner performance over the common configuration set.
Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment understanding, and closed-loop operation rather than packet delivery alone. This demo paper presents a robot-edge semantic communication (SemCom) testbed integrating robot-side visual compression, edge-side semantic mapping, and dashboard-based mission interaction. A mobile robot equipped with RGB-D sensing and LiDAR runs ROS 2, while a Jetson Orin edge node performs reconstruction, RTAB-Map mapping, semantic object handling, and browserbased visualization. As an initial proof of concept, RGB frames are encoded on the robot into VQ-VAE tokens using an ONNX Runtime encoder and reconstructed on the edge using a PyTorch decoder. A 320 X 240 image is represented by an 80 X 60 token grid with a packed payload of 5400 bytes, corresponding to a 42.67X reduction relative to model-input RGB bytes. The reconstructed visual stream is further associated with depth, pose, and 3D mapping information to generate a semantic map for downstream robotic applications. The demo exposes the full path from semantic visual transport to object-level map interaction, and provides a practical platform for future task-aware 6G networking studies at the intersection of SemCom, embodied AI, and physical AI-enabled robotics. A video of the demo is available at https://tinyurl.com/Tos09
Peizheng Li, Xinyi Lin, Sajida Gufran +1
Bristol Research and Innovation Laboratory, Toshiba Europe Ltd., U.K.
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.
Chuan-Chi Lai, Ang-Hsun Tsai
Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Department of Communications Engineering, Feng Chia University, Taichung 407102, Taiwan
Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels. In realistic collaborative sensing scenarios, spectral resources are quantized into finite physical resource blocks or orthogonal subcarriers, rendering simultaneous transmission by all agents infeasible. To address this, we propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps. We introduce Arbiter-Assisted Semantic Information Gating (A-SIG), a lightweight coordination mechanism that enforces hard access constraints by scheduling only the top-K agents based on locally computed semantic importance scores. By aggregating these prioritized observations into a compact latent state, a self-supervised global encoder enables a distributional policy to mitigate tail risks despite data sparsity. We evaluate MASK across diverse benchmarks, demonstrating that it matches the performance of communication-unconstrained baselines even when channel access is restricted to a small fraction of the swarm size. Furthermore, the framework exhibits inherent resilience to packet erasures, validating semantic scheduling as a critical enabler for resource-constrained 6G systems.
Ahmet Gunhan Aydin, Elif Tugce Ceran
Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, 06800, Turkey · Aselsan Inc., Ankara, Turkey