Multi-robot systems (MRS) rely on exchanging raw sensory data to cooperate in complex three-dimensional (3D) environments. However, this strategy often leads to severe communication congestion and high transmission latency, significantly degrading collaboration efficiency. This paper proposes a decentralized task-oriented semantic communication framework for multi-robot collaboration in unknown 3D environments. Each robot locally extracts compact, task-relevant semantics using a lightweight Pixel Difference Network (PiDiNet) with geometric processing. It shares only these semantic updates to build a task-sufficient 3D scene representation that supports cooperative perception, navigation, and object transport. Our numerical results show that the proposed method exhibits a dramatic reduction in communication overhead from $858.6$ Mb to $4.0$ Mb (over $200\times$ compression gain) while improving collaboration efficiency by shortening task completion from $1,054$ to $281$ steps.

翻译：多机器人系统依赖于交换原始感知数据以在复杂三维环境中协作。然而，这种策略常导致严重的通信拥塞与高传输延迟，显著降低协作效率。本文提出一种面向任务的分散式语义通信框架，用于未知三维环境中的多机器人协作。每个机器人通过轻量级像素差分网络结合几何处理，在本地提取紧凑的任务相关语义，并仅共享这些语义更新以构建支持协同感知、导航与物体运输的任务充分三维场景表征。数值结果表明，所提方法将通信开销从858.6 Mb大幅降低至4.0 Mb（压缩增益超过200倍），同时通过将任务完成步数从1,054步缩短至281步，显著提升了协作效率。