Share Email Print
cover

Proceedings Paper • new

UAV pursuit using reinforcement learning
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 15 May 2019
PDF: 8 pages
Proc. SPIE 11021, Unmanned Systems Technology XXI, 1102109 (15 May 2019); doi: 10.1117/12.2520310
Show Author Affiliations
Alexandre Bonnet, Univ. de Moncton (Canada)
UPSSITECH, Univ. Toulouse (France)
Moulay A. Akhloufi, Univ. de Moncton (Canada)


Published in SPIE Proceedings Vol. 11021:
Unmanned Systems Technology XXI
Charles M. Shoemaker; Hoa G. Nguyen; Paul L. Muench, Editor(s)

© SPIE. Terms of Use
Back to Top