UAV pursuit using reinforcement learning

Alexandre Bonnet; Moulay A. Akhloufi

doi:10.1117/12.2520310

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15 May 2019 UAV pursuit using reinforcement learning

Alexandre Bonnet, Moulay A. Akhloufi

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Proceedings Volume 11021, Unmanned Systems Technology XXI; 1102109 (2019) https://doi.org/10.1117/12.2520310
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States

Abstract

Over the past few years, UAVs have known and increase in popularity and are now widely used in many applications. Today, the use of multiple UAVs and UAV swarms are attracting more interest from the research community leading to the exploration of topics such as UAV cooperation, multi-drones autonomous navigation, etc. In this work, we are interested in UAVs tracking and pursuit. The goal here, is to use deep learning and the captured images from one of the UAVs to detect and track the second moving UAV. The proposed approach uses deep reinforcement learning for UAV pursuit. The input is the current frame cropped using the last target pose, and the output is a probabilistic distribution between a set of possible actions. The experimental results are promising and show that the proposed algorithm achieves high performances in challenging outdoor scenarios.

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Alexandre Bonnet and Moulay A. Akhloufi "UAV pursuit using reinforcement learning", Proc. SPIE 11021, Unmanned Systems Technology XXI, 1102109 (15 May 2019); https://doi.org/10.1117/12.2520310

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