Towards Design of a Deformable Propeller for Drone Safety

Dinh Quang Nguyen, Giuseppe Loianno, Van Anh Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drones have brought many benefits to our lives and their use is growing at a rapid rate. Many countries have drone flight restriction rules; however, the safety of drone operators and bystanders, and the protection of drones against damage require improvement. Here, we propose a novel design of deformable propellers inspired by dragonfly wings. The structure of these propellers includes a flexible segment similar to the nodus on a dragonfly wing. This flexible segment can bend, twist and even fold upon collision, absorbing force upon impact and protecting the propeller from damage. Part of the leading edge of the propeller consists of a pliable silicone rubber surface able to absorb impact forces and reducing blade sharpness. The propeller, which is approximately 10inches long, can generate a thrust force of nearly 1.3 N at maximum velocity of about 3200rpm. Results of blade sharpness tests showed that the deformable propeller was safer than a rigid propeller. After deformation upon collision, the propeller can return to its original form and work normally within 0.4 seconds.

Original languageEnglish (US)
Title of host publication2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages464-469
Number of pages6
ISBN (Electronic)9781728165707
DOIs
StatePublished - May 2020
Event3rd IEEE International Conference on Soft Robotics, RoboSoft 2020 - New Haven, United States
Duration: May 15 2020Jul 15 2020

Publication series

Name2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020

Conference

Conference3rd IEEE International Conference on Soft Robotics, RoboSoft 2020
CountryUnited States
CityNew Haven
Period5/15/207/15/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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