Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations

TL;DR

This paper introduces a novel spatial-temporal routing algorithm for high-density sUAS traffic management that considers static and dynamic obstacles, improving safety and efficiency in urban environments.

Contribution

The paper presents a new routing algorithm and simulation platform for sUAS traffic management that accounts for static obstacles, dynamic obstacles, and communication constraints in urban areas.

Findings

The proposed algorithm outperforms existing methods in processing speed.

It demonstrates improved memory efficiency and conflict avoidance.

Simulation results show better throughput and communication resource utilization.

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.