PB-STDG：面向无人机道路系统的基于预测的短期分散式贪心引导算法

中文摘要 AI 辅助

近年来，无人机（UAV）或无人驾驶飞行器越来越多地被应用于城市环境中，例如包裹递送、基础设施检查、应急响应和无人机灯光秀等场景。一个不可忽视的问题是如何管理由不同实体运营的日益增多的无人机，使它们能够协同避免潜在碰撞，并在城市空域中确定无冲突的短期飞行路径。本文提出了一种用于结构化无人机道路系统（DRS）的基于预测的短期分散式贪心（PB-STDG）引导算法。PB-STDG通过引入预测机制扩展了原始的STDG算法，该机制使无人机能够利用信标数据包中共享的附加信息来预测相邻无人机的决策，旨在解决STDG算法中观察到的过度保守行为。我们进行了多个仿真场景，以评估所提出的算法并与STDG进行比较。结果表明，PB-STDG在保持与STDG相当的安全水平的同时，提高了交通效率。

英文摘要

In recent years, Unmanned Aerial Vehicles (UAVs) or drones have been increasingly adopted in urban environments for applications such as parcel delivery, infrastructure inspection, emergency response, and drone light shows. A non-negligible issue is how to manage the increasing number of drones operated by different entities, to enable them to cooperatively avoid potential collisions and determine conflict-free short-term flight paths in urban airspace. This paper presents a Prediction-Based Short-Term Decentralized Greedy (PB-STDG) guidance algorithm for a structured Drone Road System (DRS). PB-STDG extends the original STDG algorithm by introducing a prediction mechanism that enables drones to anticipate the decisions of neighboring drones using additional information shared in beacon packets, aiming to address the over-conservative behavior observed in the STDG algorithm. Several simulation scenarios are conducted to evaluate and compare the proposed algorithm with STDG. The results show that PB-STDG improves traffic efficiency while maintaining a safety level comparable to that of STDG.

发表机构

• University of Canterbury（坎特伯雷大学）

机构由 AI 辅助整理，请以论文原文为准。