Integrating Learning, Optimization, and Prediction for Efficient Navigation of Swarms of Drones

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Amin Majd, Adnan Ashraf, Elena Troubitsyna, Masoud Daneshtalab
Editors: Ivan Merelli, Pietro Lio, Igor Kotenko
Publication year: 2018
Publisher: IEEE
Book title: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
Start page: 101
End page: 108
ISBN: 978-1-5386-4976-3
eISBN: 978-1-5386-4975-6
ISSN: 2377-5750


Swarms of drones are increasingly been used in a variety of monitoring and surveillance, search and rescue, and photography and filming tasks. However, despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to generate efficient drone routes while minimizing the risks of drone collisions. In this paper, we present a novel approach that integrates learning, optimization, and prediction for generating efficient and safe routes for swarms of drones. The proposed approach comprises three main components: (1) a high-performance dynamic evolutionary algorithm for optimizing drone routes, (2) a reinforcement learning algorithm for incorporating the feedback and runtime data about the system state, and (3) a prediction approach to predict the movement of drones and moving obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results demonstrate that the proposed approach allows to significantly reduce the route lengths and computation overhead while producing efficient and safe routes.


Last updated on 2020-21-01 at 04:35