关键具身智能驱动磁浮交通系统智能化：前沿进展、应用与未来挑战

徐俊起; 李凤恺; 陈琛; 孙友刚; 荣立军

doi:10.3969/j.issn.0258-2724.20260003

关键具身智能驱动磁浮交通系统智能化：前沿进展、应用与未来挑战

doi: 10.3969/j.issn.0258-2724.20260003

徐俊起^{1, 2, 3,},
李凤恺^{1, 3},
陈琛^{1, 2},
孙友刚^{1, 2},
荣立军^{1, 2, ,}

1.
同济大学交通学院，上海 201804
2.
同济大学磁浮交通工程技术研究中心，上海 201804
3.
同济大学高速磁浮运载技术全国重点实验室，上海 201804

基金项目: 国家自然科学基金项目（52232013，52502449）

详细信息

作者简介:
徐俊起（1977—），男，研究员，博士，E-mail：xujunqi@tongji.edu.cn

通讯作者:
荣立军（1974—），男，副研究员，E-mail： ronglijun@tongji.edu.cn

中图分类号: U237；U266.4
计量
- 文章访问数: 82
- HTML全文浏览量: 77
- PDF下载量: 13
- 被引次数: 0
出版历程
- 收稿日期: 2026-01-04
- 修回日期: 2026-04-20
- 网络出版日期: 2026-06-02

Intelligentization of Maglev Transportation Systems Driven by Critical Embodied Intelligence: Recent Advances, Applications, and Future Challenges

XU Junqi^{1, 2, 3
,},
LI Fengkai^{1, 3},
CHEN Chen^{1, 2},
SUN Yougang^{1, 2},
RONG Lijun^{1, 2
, ,}

1.
College of Transportation Engineering, Tongji University, Shanghai 201804, China
2.
National Maglev Transportation Engineering R & D Center, Tongji University, Shanghai 201804, China
3.
State Key Laboratory of High-speed Maglev Transportation Technology, Tongji University, Shanghai 201804, China

摘要

摘要:
磁浮交通系统凭借非接触、高速度和低噪音等优势成为未来轨道交通的重要发展方向，其复杂运行环境、高安全要求与高运维成本对系统的自主感知、决策与控制能力提出更高要求. 在此背景下，关键具身智能作为新一代人工智能的重要范式，强调智能体（如车载智能控制体、轨道侧感知智能体与机器人）与物理环境之间的实时交互与持续学习，推动感知、决策与控制的深度融合，为磁浮交通系统智能化发展提供了新的技术路径. 在具体应用中，关键具身智能首先赋能运行安全与悬浮导向精准控制，通过融合多模态传感器数据动态构建车辆-轨道-环境态势，实现毫米级感知、状态预测以及悬浮导向与牵引制动策略的自主生成，确保极端工况下的稳定性与舒适性；其次，在基础设施自主巡检与智能维护领域，具身智能驱动的机器人或无人机可替代人工执行高风险任务，通过交互式检测识别缺陷，并基于经验预测部件寿命，优化维护周期；最后，在全局调度与协同优化方面，多个具身智能体构成分布式系统，通过共享局部感知信息，动态协商调整运行图，实现列车群高密度、协同化与能效优化运行. 综上，关键具身智能正推动磁浮交通系统从“自动化执行”向“自主化进化”转变，在提升安全性、运行效率与系统韧性的同时，有助于降低全生命周期成本，是构建下一代自适应、可进化智慧轨道交通的关键路径. 未来研究需聚焦于智能体的高可靠性验证、多智能体协同机制及在复杂环境下的鲁棒性提升，以加速其工程应用进程.
- 关键具身智能 /
- 磁浮交通系统 /
- 多模态融合感知 /
- 自主协同控制 /
- 智能运维
Abstract:
Maglev transportation systems have become an important development direction for future rail transit by virtue of their advantages such as non-contact operation, high speed, and low noise; however, their complex operating environments, high safety requirements, and high operation and maintenance costs put forward higher requirements for the autonomous perception, decision-making, and control capabilities of the systems. In this context, critical embodied intelligence, as an important paradigm of next-generation artificial intelligence, emphasizes the real-time interaction and continuous learning between intelligent agents (such as onboard intelligent control agents, trackside sensing agents, and robots) and the physical environment, promotes the deep integration of perception, decision-making, and control, and provides a new technical pathway for the intelligent development of maglev transportation systems. In specific applications, critical embodied intelligence firstly empowers operational safety and the precise control of levitation and guidance. By fusing multimodal sensor data to dynamically construct the vehicle-track-environment situation, it achieves millimeter-level perception, state prediction, and the autonomous generation of levitation-guidance and traction-braking strategies, ensuring stability and comfort under extreme working conditions; secondly, in the field of autonomous infrastructure inspection and intelligent maintenance, robots or unmanned aerial vehicles driven by embodied intelligence can replace manual labor to execute high-risk tasks, identify defects through interactive detection, predict component lifespans based on experience, and optimize maintenance cycles; finally, in terms of global scheduling and collaborative optimization, multiple embodied intelligent agents constitute a distributed system, and by sharing local perception information, they dynamically negotiate and adjust timetables to achieve high-density, collaborative, and energy-efficiency optimized operations of train groups. In summary, critical embodied intelligence is driving the transformation of maglev transportation systems from “automated execution” to “autonomous evolution”. While enhancing safety, operational efficiency, and system resilience, it helps to reduce full life-cycle costs and is a key pathway to constructing the next-generation adaptive and evolvable intelligent rail transit. Future research should focus on the high-reliability validation of intelligent agents, multi-agent collaboration mechanisms, and robustness enhancement in complex environments to accelerate their engineering application process.
- critical embodied intelligence /
- maglev transportation system /
- multimodal fusion perception /
- autonomous collaborative control /
- intelligent operation and maintenance

HTML全文

图 1 分布式智能网络模型

Figure 1. Distributed intelligent network model

下载: 全尺寸图片幻灯片

图 2 车载智能控制体示意

Figure 2. Schematic diagram of onboard intelligent control agent

下载: 全尺寸图片幻灯片

图 3 轨道侧感知智能体示意

Figure 3. Schematic diagram of trackside sensing agent

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图 4 移动作业智能体示意

Figure 4. Schematic diagram of mobile operation agent

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图 5 关键具身智能“边缘-网络-平台”系统集成框架

Figure 5. System integration framework of “edge-network-platform” for critical embodied intelligence

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图 6 关键具身智能的核心应用范式示意

Figure 6. Schematic diagram of core application paradigms of critical embodied intelligence

下载: 全尺寸图片幻灯片

图 7 超高速与真空管道具身智能适应性挑战示意

Figure 7. Schematic diagram of adaptability challenges of embodied intelligence in ultra-high-speed and evacuated tubes

下载: 全尺寸图片幻灯片

图 8 高速磁浮交通关键具身智能研究展望示意

Figure 8. Schematic diagram of research prospects of critical embodied intelligence for high-speed maglev transportation

下载: 全尺寸图片幻灯片

表 1 关键具身智能载体技术特性对比

Table 1. Comparison of technical characteristics of carriers for critical embodied intelligence

载体	计算能力	实时要求	核心职能	典型技术配置
车载智能控制体	高性能边缘计算	毫秒级	悬浮/导向/牵引控制、实时决策	SoC + 多传感器融合与实时控制算法
轨道侧感知智能体	中等边缘算力	秒级	环境监测、状态识别、区域协同预警	嵌入式 GPU + 轻量化诊断模型
移动作业智能体	嵌入式自主计算	分钟级	巡检、维护作业	移动平台 + 感知系统 + 作业执行机构

下载: 导出CSV

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