考虑通信和空域安全的无人机交通管理实时路由仿真

Zhao Jin, Ziyi Zhao, Chen Luo, Franco Basti, A. Solomon, M. C. Gursoy, Carlos Caicedo, Qinru Qiu
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引用次数: 5

摘要

小型无人机系统(sUAS)将在不久的将来成为智慧城市和智能交通环境的重要组成部分。预计未来几年,商业交付和土地测量等sUAS相关应用的需求将迅速增长。一般来说,需要sUAS交通路由和管理功能来协调不同发射场的sUAS发射,并确定其轨迹以避免冲突,同时考虑其他几个约束,如预期到达时间、最小飞行能量和通信资源的可用性。然而,随着某一区域机载sUAS密度的增加,很难预见潜在的空域和通信资源冲突并立即做出决策以避免冲突。为了应对这一挑战,我们提出了一种时空路由算法和仿真平台,用于高密度城市地区的sUAS轨迹管理,该算法和仿真平台可以在考虑静态或动态障碍物的时空迷宫中规划sUAS运动。路由允许sUAS避开静态禁飞区(即静态障碍物)或其他飞行中的sUAS和通信资源拥挤的区域(即动态障碍物)。在基于智能体的仿真平台上对算法进行了评估。仿真结果表明,该算法在许多方面都优于其他路由管理算法,特别是在处理速度和存储效率方面。详细比较了无人机的飞行时间、总吞吐量、冲突率和通信资源利用率。结果表明,该算法可用于解决下一代智慧城市和智能交通对空域和通信资源的利用需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations
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.
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