发表机构
University of Leicester（莱斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对搜索救援中异构机器人覆盖规划问题，提出边缘中心语义感知框架，通过语义网格地图共享和能力感知路径分配，实现91.5%覆盖率且无不可行分配，并大幅降低通信载荷。
AI 中文摘要
搜索与救援（SAR）行动日益部署由空中和地面机器人组成的异构团队。然而，传统覆盖方法通常不能将感知到的地形转化为平台特定的可达性，而连续图像交换则带来高昂的通信成本。我们提出了一种边缘中心、语义感知的覆盖规划框架，该框架集成了空中地形感知、机器人特定可通行性推理和载荷高效的语义状态共享。空中观测被转换为紧凑的语义网格地图，从而实现受可达性约束的区域分解和能力感知的覆盖路径，这些路径仅分配每个机器人能力档案所允许的区域。由此产生的感知-共享-规划循环将语义修正反馈到可通行性推理和重新规划中，形成一种应用级机制，其动机源于为AI原生6G网络设想的AI赋能目标导向通信。在高更新场景下，传输语义修正相比周期性全地图共享可将应用载荷减少约82倍。在匹配的基准场景中，所提出的方法实现了91.5%的覆盖率且无能力不可行分配，而LS-MCPP的覆盖率为78.8%，能力不可行分配率为21.5%。语义修正更新共享的规划状态，无需重复传输完整地图。
英文摘要
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.
CommentsAn alternative version of this work was accepted for presentation at IEEE CSCN 2026
