考虑通信和空域安全的UAS流量管理实时的路由仿真
Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations
摘要：
小型无人机系统（sUAS）将成为不久的将来智慧城市和智能交通环境的重要组成部分。对sUAS相关应用的需求，如商业交付和土地测量，预计将在未来几年迅速增长。通常，需要sUAS交通路由和管理功能来协调从不同发射场发射sUAS并确定其轨迹以避免冲突，同时考虑几个其他约束，例如预期到达时间、最小飞行能量和通信资源的可用性。然而，随着机载无人机密度在一定区域内的增长，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它们。为了应对这一挑战，我们提出了一个时间和空间的路由算法和仿真平台，在高密度的城市地区，规划无人机系统的运动在空间和时间的迷宫考虑到无论是静态或动态的障碍物的轨迹管理。该路由允许sUAS避开静态禁飞区域（即静态障碍物）或其他飞行中的sUAS和具有拥塞的通信资源的区域（即动态障碍物）。该算法使用基于代理的仿真平台进行评估。仿真结果表明，该算法在处理速度和内存利用率等方面优于其他路由管理算法。详细的比较sUAS的飞行时间，总吞吐量，冲突率和通信资源利用率。结果表明，我们提出的算法可以用于解决下一代智能城市和智能交通的空域和通信资源利用需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
