[论文解读] Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations

Jin Zhao, Ziyi Zhao+6 more|arXiv (Cornell University)|Feb 27, 2020

Robotic Path Planning Algorithms参考文献 23被引用 6

一句话总结

本文提出了一种稀疏表示的时空（SRTS）路由算法，用于在高密度城市环境中实现无人机（UAS）交通的实时管理，整合空域安全与通信资源约束。该算法实现了节能、无碰撞的飞行轨迹，相比传统T-S路由，其路由速度提升84%，内存使用降低70%，在仿真中确保100%的通信连通性与零冲突。

ABSTRACT

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic routing and management functions are needed to coordinate the launching of sUAS from different launch sites and determine their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airborne sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm and simulation platform for sUAS trajectory management in a high density urban area that plans sUAS movements in a spatial and temporal maze taking into account obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms other route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used to address the airspace and communication resource utilization needs for a next generation smart city and smart transportation.

研究动机与目标

• 为应对在城市区域管理高密度小型无人机（sUAS）交通的日益增长的挑战，同时确保空域安全与通信可靠性。
• 开发一种实时、集中式的路由算法，主动避免冲突，并保证蜂窝连接的sUAS的通信连通性。
• 通过减少轨迹转弯次数，最小化控制推力，提升能源效率。
• 评估所提算法在速度、内存效率和资源利用率方面相较于现有路由方法的性能表现。
• 实现可扩展的、自主的UAS交通管理，以支持未来智慧城市与智能交通系统。

提出的方法

• SRTS路由算法采用动态与静态障碍物的稀疏表示，仅存储当前及未来的环境数据，以降低内存开销。
• 将空域建模为时空网格，整合静态禁飞区与动态障碍物（如其他sUAS和通信拥塞区域）。
• 通过最小化轨迹转弯次数，优化飞行距离与控制推力，实现最小化。
• 通过集中式、预发布路由引擎，在每次sUAS起飞前计算无碰撞、保障连通性的飞行轨迹。
• 系统与多智能体仿真平台（MATRUS）集成，以在不同交通密度和约束条件下评估性能。
• 路由引擎通过即时刷新机制应用实时环境更新，确保对动态变化的适应能力。

实验结果

研究问题

• RQ1 如何在高密度城市环境中实时优化UAS路由，以同时确保空域安全与通信资源可用性？
• RQ2 与传统时空（T-S）路由相比，所提出的SRTS算法在减少路由计算时间和内存使用方面达到何种程度？
• RQ3 SRTS算法对高密度sUAS运行中的飞行时间、冲突率、吞吐量和通信连通性有何影响？
• RQ4 禁飞区与动态通信拥塞的引入如何影响路由算法的可扩展性与性能？
• RQ5 在真实的都市交通条件下，SRTS算法能否支持100%的连通性与零冲突率？

主要发现

• 与基线T-S路由相比，SRTS路由算法将平均路由时间减少了84.12%，在高密度场景下仅需0.37ms。
• SRTS将内存使用量降低70%以上，高密度场景下平均仅使用725MB，而T-S路由为3513MB（含禁飞区）。
• 该算法在高密度交通（每10秒生成一架sUAS）下仍保持100%通信连通性，并完全消除潜在冲突。
• 与未管理场景相比，飞行时间增加不足2.74%，吞吐量仅因冲突避免与资源限制降低23.3%。
• 在轻度交通下，禁飞区使内存使用增加5.5%；在高密度交通下，增加10.5%，表明具有良好的可扩展性。
• SRTS算法在所有测试交通条件下均实现了100%的成功率，可找到可行、无冲突且保障连通性的飞行轨迹。