A Review of Cross-modal Coordination and Intelligent Scheduling for Low-altitude Three-dimensional Transportation
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摘要: 随着低空立体交通系统进程的加速推进, 空中、地面与水面三维异构交通平台的协同愈加关键. 智能调度与资源优化的深度融合, 正逐步成为智慧城市建设与应急响应体系中不可或缺的核心支撑力量. 围绕多模式协同调度的研究热点展开综述. 首先, 综合回顾了无人机、无人车与无人艇在多源信息融合、环境感知与自适应决策方面的协同机制. 其次, 从任务分解、路径规划、协同控制与系统调度四个层面, 总结了强化学习、图优化、进化算法等在复杂动态环境下的典型调度方法. 进一步地, 梳理了集中式、分布式与混合式控制架构下的任务分配与通信策略, 并分析了传感器数据、仿真数据与运行大数据在调度优化中的作用. 最后, 探讨了当前低空立体交通系统在资源分配、安全保障与跨域协同中的关键挑战, 并展望了基于大数据驱动与智能增强的低空立体交通系统未来发展路径.Abstract: As the advancement of low-altitude three-dimensional transportation systems accelerates, the collaboration among aerial, ground, and waterborne heterogeneous transportation platforms becomes increasingly critical. The seamless integration of intelligent scheduling and resource optimization is progressively establishing itself as an indispensable pillar for the development of smart cities and emergency response frameworks. A comprehensive review of the research frontiers in multi-modal collaborative scheduling is provided. Firstly, it systematically examines the collaborative mechanisms of unmanned aerial vehicles, unmanned ground vehicles, and unmanned surface vehicles in multi-source information fusion, environmental perception, and adaptive decision-making. Secondly, this paper synthesizes typical scheduling methodologies in complex dynamic environments, addressing task decomposition, path planning, collaborative control, and system-level scheduling, with a particular focus on reinforcement learning, graph optimization, and evolutionary algorithms. Furthermore, it explores task allocation and communication strategies within centralized, distributed, and hybrid control architectures, and evaluates the role of sensor data, simulation data, and operational big data in optimization of scheduling processes. Finally, the critical challenges faced by current low-altitude three-dimensional transportation systems in resource allocation, security assurance, and cross-domain collaboration are identified, and insights into the future development trajectories of these systems, underpinned by big data-driven solutions and intelligent enhancement are offered.
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图 1 临时应急空天通信示意图
Fig. 1 Schematic diagram of temporary emergency space-air communication
图 2 无人机群SAR多角度观测感知成像过程示意图
Fig. 2 Schematic diagram of multi-angle observation, perception and imaging process of unmanned aerial vehicle swarm SAR
图 3 基于概率视距信道的无人机辅助多地面终端操作框架示意图
Fig. 3 Schematic diagram of the unmanned aerial vehicle-assisted multi-ground terminal operation framework based on probabilistic line-of-sight channels
图 4 无人机辅助无线网络中通信下行数据传输示意图
Fig. 4 Schematic diagram of downlink data transmission in unmanned aerial vehicle-assisted wireless networks
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