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

Amin Majd, Adnan Ashraf, Elena Troubitsyna, Masoud Daneshtalab

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

13 Citations (Scopus)
10 Downloads (Pure)


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.

Original languageUndefined/Unknown
Title of host publication2018 IEEE Congress on Evolutionary Computation (CEC)
EditorsCarlos Coello Coello, Hisao Ishibuchi, Li Xiaodong, Fernando Von Zuben
ISBN (Electronic)978-1-5090-6017-7
ISBN (Print)978-1-5090-6018-4
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Congress on Evolutionary Computation (CEC) - IEEE Congress on Evolutionary Computation (CEC)
Duration: 8 Jul 201813 Jul 2018


ConferenceIEEE Congress on Evolutionary Computation (CEC)

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