统一多机器人神经推理框架，解决异构设备协作难题
NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
- 采用双聚合机制融合信息，支持广播与归约两种通信模式
- 实测在空地混合机器人团队中稳定运行，支持多种任务结构
- 开源跨平台框架，适合研究多机器人协同感知与控制
在异构机器人上部署学习型多机器人模型面临硬件差异、通信限制和缺乏统一执行栈的挑战。本文提出NeuroMesh，一种跨领域、跨平台、模块化的去中心化神经推理框架，统一了观测编码、消息传递、聚合和任务解码流程。NeuroMesh结合基于归约和广播的信息融合双聚合范式，并采用并行化架构，将周期时间与端到端延迟解耦。高性能C++实现利用Zenoh进行机器人间通信，支持混合GPU/CPU推理。我们在包含空中与地面机器人的异构团队上验证了NeuroMesh，涵盖协同感知、去中心化控制和任务分配，证明其在不同任务结构和负载大小下均具备鲁棒性。计划将NeuroMesh作为开源框架发布。
原文摘要 · Abstract (English)
Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack of a unified execution stack. This paper presents NeuroMesh, a multi-domain, cross-platform, and modular decentralized neural inference framework that standardizes observation encoding, message passing, aggregation, and task decoding in a unified pipeline. NeuroMesh combines a dual-aggregation paradigm for reduction- and broadcast-based information fusion with a parallelized architecture that decouples cycle time from end-to-end latency. Our high-performance C++ implementation leverages Zenoh for inter-robot communication and supports hybrid GPU/CPU inference. We validate NeuroMesh on a heterogeneous team of aerial and ground robots across collaborative perception, decentralized control, and task assignment, demonstrating robust operation across diverse task structures and payload sizes. We plan to release NeuroMesh as an open-source framework to the community.
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