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Using Optimization, Learning, and Drone Reflexes to Maximize Safety of Swarms of Drones
Faculty of Natural Sciences and Technology, Åbo Akademi University, Turku, Finland.
Faculty of Natural Sciences and Technology, Åbo Akademi University, Turku, Finland.
Faculty of Natural Sciences and Technology, Åbo Akademi University, Turku, Finland.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2018 (English)In: 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
drone, evolutionary algorithms, machine learning, path planning, reflexes, Safety, swarm, Accident prevention, Drones, Learning algorithms, Learning systems, Motion planning, Neurophysiology, Reinforcement learning, Parallel implementations, Performance dynamics, Safe operation
National Category
Computer Sciences Computer Systems Computer Engineering Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mdh:diva-41393DOI: 10.1109/CEC.2018.8477920ISI: 000451175500046Scopus ID: 2-s2.0-85056256322ISBN: 9781509060177 (print)OAI: oai:DiVA.org:mdh-41393DiVA, id: diva2:1379042
Conference
2018 IEEE Congress on Evolutionary Computation, CEC 2018, 8 July 2018 through 13 July 2018
Available from: 2019-12-16 Created: 2019-12-16 Last updated: 2020-01-10Bibliographically approved

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Daneshtalab, Masoud

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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