面向集群机器人作业的通信−控制−计算融合系统综述

何佳闻; 颜志; 王耀南; 江一鸣; 王涛; 贺文斌; 方峥

doi:10.16383/j.aas.c250263

面向集群机器人作业的通信−控制−计算融合系统综述

doi: 10.16383/j.aas.c250263 cstr: 32138.14.j.aas.c250263

何佳闻^{1, 2,},
颜志^{2, 3, 5,},
王耀南^{1, 2,},
江一鸣^{1, 2,},
王涛^{1, 2,},
贺文斌^{1, 2,},
方峥^{4, 5,}

1.
湖南大学人工智能与机器人学院长沙 410082
2.
湖南大学机器人视觉感知与控制技术国家工程研究中心长沙 410082
3.
湖南大学电气与信息工程学院长沙 410082
4.
湖南大学−中国移动产业智能联合研究院长沙 410012
5.
中国移动通信集团湖南有限公司长沙 410100

基金项目: 国家自然科学基金(62293510, 62293511), 湖南大学−中国移动产业智能联合研究院揭榜挂帅“重大装备智造网络化机器人加工系统(5G+ 机器人)”, 面向6G的智能体通信场景需求及关键技术研究(2025ZD1304700)资助

详细信息

作者简介:
何佳闻：湖南大学人工智能与机器人学院博士研究生. 2022年获得中南大学硕士学位. 主要研究方向为集群机器人的通信−控制−计算联合系统优化, 机器人网络化技术研究. E-mail: hjwdaxia@hnu.edu.cn

颜志：湖南大学电气与信息工程学院教授. 2012年获得北京邮电大学博士学位. 主要研究方向为无线通信系统(5G/6G)的新理论与技术, 机器人通信与组网. 本文通信作者. E-mail: yanzhi@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学人工智能与机器人学院教授. 1995年获得湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

江一鸣：湖南大学人工智能与机器人学院教授. 主要研究方向为多机器人协同控制及应用. E-mail: ymjiang@hnu.edu.cn

王涛：湖南大学人工智能与机器人学院博士研究生. 2023年获得北京信息科技大学硕士学位. 主要研究方向为边缘计算, 云−边−端协作和传输与计算一体化. E-mail: wangtao565@hnu.edu.cn

贺文斌：湖南大学人工智能与机器人学院博士后. 主要研究方向为机器人作业状态感知、检测与故障诊断. E-mail: hwenbin@hnu.deu.cn

方峥：中国移动通信集团湖南有限公司高级工程师. 2023年获得北京大学博士学位. 主要研究方向为机器人先进传感器与电子器件. E-mail: fangzheng@hn.chinamobile.com

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- 文章访问数: 11
- HTML全文浏览量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2025-06-18
- 录用日期: 2026-01-29
- 网络出版日期: 2026-05-25

Survey on the Integrated Communication, Control, and Computation Systems for Clustered Robotic Operations

HE Jia-Wen^{1, 2
,},
YAN Zhi^{2, 3, 5
,},
WANG Yao-Nan^{1, 2
,},
JIANG Yi-Ming^{1, 2
,},
WANG Tao^{1, 2
,},
HE Wen-Bin^{1, 2
,},
FANG Zheng^{4, 5
,}

1.
School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082
2.
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082
3.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
4.
Hunan University-China Mobile Industrial Intelligence Joint Research Institute, Changsha 410012
5.
China Mobile Communications Group Hunan Company Limited, Changsha 410100

Funds: Supported by National Natural Science Foundation of China (62293510, 62293511), Hunan University-China Mobile Industrial Intelligence Joint Research Institute Unveils Key Project “Networked Robotic Machining Systems for Intelligent Manufacturing of Advanced Equipment (5G+Robotics)” via Open Solicitation, and Research on Scenario Needs and Key Technologies for 6G-Oriented Agent Communications (2025ZD1304700)

More Information

Author Bio:
HE Jia-Wen　Ph. D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Central South University in 2022. His research interests include the joint system optimization of communication-control-computation for clustered robots and the study of robotic networking technologies

YAN Zhi　Professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph. D. degree from Beijing University of Posts and Telecommunications in 2012. His research interests include new theories and technologies in wireless communication systems (5G/6G) and robot communication and networking. Corresponding author of this paper

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the School of Artificial Intelligence and Robotics, Hunan University. He received his Ph. D. degree from Hunan University in 1995. His research interests include robotics, intelligent control, and image processing

JIANG Yi-Ming　Professor at the School of Artificial Intelligence and Robotics, Hunan University. His research interests include covers multiple robots cooperative control and its application

WANG Tao　Ph. D. candidate at the School of Artificial Intelligence and Robotics, Hunan University. He received his master degree from Beijing Information Science and Technology University in 2023. His research interests include edge computing, cloud-edge-end collaboration, and integrated transmission and computing

HE Wen-Bin　Postdoctor at the School of Artificial Intelligence and Robotics, Hunan University. His research interests include robotic operation condition perception, detection, and fault diagnosis

FANG Zheng　senior engineer at China Mobile Communications Group Hunan Company Limited. He received his Ph.D. from Peking University in 2023. His research interests include advanced sensors and electronic devices for robotics

摘要

摘要: 在集群机器人系统中, 通信、控制与计算模块的协同设计已成为提升系统整体性能的关键, 对推动离散制造自动化向智能化、集成化方向发展具有重要促进作用. 首先, 从集群机器人“通−控−算”联合系统架构出发, 通过梳理国内外在“通−控”、“通−算”、“控−算”融合技术方面的研究现状, 揭示子系统之间的耦合关系, 强调“通−控−算”联合设计对提升集群机器人整体作业性能的重要性. 接着, 以集群机器人系统在“通−控−算”软件仿真、硬件在环仿真及实物测试验证平台为例, 总结具体实施中的关键技术. 最后, 对集群机器人“通−控−算”系统联合设计的未来研究方向进行总结与展望.
- 集群机器人 /
- 通信−控制−计算融合 /
- 工业网络系统 /
- 联合设计 /
- 工业物联网
Abstract: The co-design of communication, control, and computing modules in clustered robotic systems has emerged as a critical enabler for enhancing overall system performance, playing a significant role in advancing discrete manufacturing automation toward intelligent and integrated development. Firstly, this paper starts with the architecture of the “communication-control-computation” integrated system for clustered robots. By reviewing the current research on the fusion technologies of “communication-control”, “communication-computation”, and “control-computation” both domestically and internationally, the coupling relationships between subsystems are revealed, highlighting the importance of “communication-control-computation” co-design in improving the overall operational performance of clustered robots. Next, it summarizes key technologies in the implementation process through examples of software simulations, hardware-in-the-loop simulations, and physical verification platform for the “communication-control-computation” system in clustered robots. Finally, it concludes with a summary and outlook on future research directions for the co-design of the “communication-control-computation” system in clustered robots.
- clustered robots /
- integration of communication-control-computation /
- industrial network systems /
- co-design /
- industrial internet of things

HTML全文

图 1 集群机器人作业系统的三大核心子系统: 通信、控制与计算

Fig. 1 The three core subsystems of the clustered robotic operation system: Communication, control, and computing

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图 2 工业无线通信系统的SDN/NFV架构

Fig. 2 SDN/NFV-based architecture for industrial wireless communication system

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图 3 集群机器人网络化控制

Fig. 3 Networked control of clustered robots

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图 4 具身智能与大模型的云−边−端协同架构

Fig. 4 Cloud–edge–end architecture for embodied intelligence with large models

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图 5 通信−控制−计算协同设计框架

Fig. 5 Communication-control-computing co-design framework

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图 6 通信−控制−计算仿真软件搭建流程

Fig. 6 Construction process of communication-control-computing simulation software

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图 7 文献[26]的网络硬件在环仿真框架

Fig. 7 The network hardware-in-the-loop simulation framework in reference [26]

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图 8 通信−控制−计算协同设计实例平台

Fig. 8 Example platform for communication-control-computing co-design

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表 1 云−边−端计算层级与异构硬件特性对比

Table 1 Comparison of cloud–edge–end computing layers and heterogeneous hardware characteristics

层级/硬件	典型算力水平	时延特性	功耗与成本	适用任务类型
端侧(ASIC/SoC)	低$ \sim $中(实时处理优化)	确定性时延	功耗最低, 成本低	本地控制、数据预处理、动作响应
边缘(CPU/GPU/FPGA)	中$ \sim $高(并行推理)	毫秒级低时延(小于10 ms)	中等功耗, 中等成本	SLAM、多传感器融合、局部规划
云端(集群GPU/CPU)	最高(大规模并行)	跨网高时延(大于30 ms)	功耗最高, 成本高	全局规划、模型训练、工艺分析

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表 2 通信−控制耦合参数及其对系统性能的影响

Table 2 Communication–control coupling parameters and their impacts

耦合参数	对通信的影响	对控制系统的影响	文献
信道质量	影响误码率, 吞吐下降	反馈噪声增加, 影响准确性与稳定性	[25, 28]
移动速度	信道时变性增强, 质量波动	增大控制误差, 影响稳定性和可用控制增益	[25, 31]
采样周期	高频采样增加通信负载, 易引发拥塞	高频采样易引发不稳定	[32−34]
控制增益	高增益对链路可靠性要求更高	高增益更易受时延/丢包影响, 降低稳定性	[35−36]
通信时延	增加传输等待时间, 降低链路有效性	降低响应速度与准确性, 增大不稳定概率	[37−38]
时延抖动	链路不确定性上升	引起控制抖动、降低控制鲁棒性与稳定性	[39−40]
丢包率	可靠性下降, 数据难以稳定到达	准确性下降, 连续丢包可能导致系统失稳	[41]
通信带宽	限制数据速率, 影响可靠性	限制采样频率与控制刷新率, 影响响应速度与精度	[42]
信息新鲜度	反映链路更新是否及时	AoI高导致状态反馈过时, 影响响应速度	[43−44]

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表 3 通信−控制−计算协同设计目标和约束

Table 3 Communication-control-computing co-design objectives and constraints

			通信约束							控制约束			计算约束
设计方法	文献	优化目标	时延	传输功率	传输速率	带宽	丢包率	中断概率	通信成本	稳定性	收敛性	控制成本	CPU	计算卸载	计算成本
通信−控制	[30]	最小化通信能耗	-	-	-	-	-	-	-	-	-	√	-	-	-
	[44]	最小化AoI	√	√	-	-	√	-	-	-	√	-	-	-	-
	[45]	最小化控制成本	√	-	-	√	√	-	-	-	-	-	-	-	-
	[48]	最小化通信需求	-	-	-	-	-	-	-	√	-	-	-	-	-
	[49]	最大化频谱效率	√	√	-	√	-	√	-	-	√	-	-	-	-
	[50]	最小化信噪比	-	-	-	-	-	-	-	√	-	-	-	-	-
	[51]	最小化通−控成本	√	-	-	-	√	-	-	√	-	-	-	-	-
通信−计算	[69]	最小化通信能耗	√	√	-	-	-	-	-	-	-	-	-	√	√
	[70]	最小化通−算能耗	√	√	-	-	-	-	-	-	-	-	-	√	-
	[72]	最小化时延	√	√	√	-	-	-	-	-	-	-	√	√	√
	[73]	最小化时延	√	√	√	√	-	-	√	-	-	-	-	√	√
	[78]	最小化AoI	√	-	-	-	-	-	-	-	-	-	-	√	-
控制−计算	[88]	最小化计算延迟	-	-	-	-	-	-	-	√	√	-	√	-	-
	[89]	最小化计算复杂度	-	-	-	-	-	-	-	√	√	√	√	-	-
	[90]	最小化计算能耗	-	-	-	-	-	-	-	√	-	√	-	-	√
通−控−算	[68]	最小化通−控−算能耗	√	√	-	-	-	-	-	√	-	-	√	√	-
	[69]	最小化控制成本	√	-	√	√	√	-	√	-	√	-	√	-	-
	[112]	最小化整体时延	√	-	-	-	√	-	-	√	√	-	√	-	-
	[113]	最大化任务收益	√	-	√	√	-	-	-	-	√	-	√	√	√

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表 4 通信−控制−计算协同验证方法及典型工具对比

Table 4 Comparison of communication–control–computing validation methods and typical tools

验证方式	核心特点	适用研究任务	主要局限性	常用软件/设备
				控制: Gazebo, CoppeliaSim
软件仿真	高可控性、建模灵活	架构验证、大规模协同	模型简化, 难反映真实	通信: ns-3, OMNeT++
				计算: EdgeCloudSim, iFogSim
				控制: PLC
硬件在环	真实链路/硬件参与	实时策略验证	规模受限	通信: USRP+OAI
				计算: 边缘服务器
实物测试	全链路真实; 系统级性能	工业协同作业	成本高、实时调度难	集群机器人、5G、ROS2、边缘节点

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