Using Optimization, Learning, and Drone Reflexes to Maximize Safety of Swarms of Drones

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Amin Majd, Adnan Ashraf, Elena Troubitsyna, Masoud Daneshtalab
Editors: Carlos Coello Coello, Hisao Ishibuchi, Xiaodong Li, Fernando Von Zuben
Publication year: 2018
Publisher: IEEE
Book title: 2018 IEEE Congress on Evolutionary Computation (CEC)
Start page: 1
End page: 8
ISBN: 978-1-5090-6018-4
eISBN: 978-1-5090-6017-7


Despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to maximize the safety of swarms of drones by minimizing the risks of drone collisions. In this paper, we present an approach that uses optimization, learning, and automatic immediate responses (reflexes) of drones to ensure safe operations of swarms of drones. The proposed approach integrates a high-performance dynamic evolutionary algorithm and a reinforcement learning algorithm to generate safe and efficient drone routes and then augments the generated routes with dynamically computed drone reflexes to prevent collisions with unforeseen obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results show that the proposed approach maximizes safety and generates highly efficient drone routes.


Last updated on 2020-14-07 at 06:22