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arXiv · 2610.05122

PB-STDG: A Prediction-Based Short-Term Decentralized Greedy Guidance Algorithm for a Drone Road System

Zhouyu Qu·Andreas Willig·Xiaobing Wu
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Abstract

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.

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Zhouyu Qu, Andreas Willig, Xiaobing Wu. 2026-10-04. PB-STDG: A Prediction-Based Short-Term Decentralized Greedy Guidance Algorithm for a Drone Road System. https://arxiv.org/abs/2610.05122

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