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Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Authors :
Jinpeng Zhang
Honghai Zhang
Jinlun Zhou
Mingzhuang Hua
Gang Zhong
Hao Liu
Source :
Drones, Vol 7, Iss 8, p 491 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time.

Details

Language :
English
ISSN :
2504446X
Volume :
7
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Drones
Publication Type :
Academic Journal
Accession number :
edsdoj.b0f74bc040524c1aac5ead0eed2238cd
Document Type :
article
Full Text :
https://doi.org/10.3390/drones7080491