Deep Reinforcement Learning based Collision Avoidance in UAV Environment

Sihem Ouahouah, Miloud Bagaa, Jonathan Prados-Garzon, Tarik Taleb

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

13 Sitaatiot (Scopus)


Unmanned Aerial Vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt Visual Line of Sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs’ commercial operations to cover the urban and populated area controlled airspace Beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs’ autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a Multi-Access Edge Computing (MEC) that can read the gathered data from different UAV’s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This paper proposes probabilistic and Deep Reinforcement Learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.

JulkaisuIEEE Internet of Things Journal
DOI - pysyväislinkit
TilaSähköinen julkaisu (e-pub) ennen painettua julkistusta - 8 lokak. 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu


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