4.5 Article

Bird Velocity Optimization as Inspiration for Unmanned Aerial Vehicles in Urban Environments

Journal

AIAA JOURNAL
Volume 59, Issue 7, Pages 2503-2516

Publisher

AMER INST AERONAUTICS ASTRONAUTICS
DOI: 10.2514/1.J059438

Keywords

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Funding

  1. European Research Council under the European Union's Horizon 2020 research and innovation programme [679355]
  2. EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems at the Bristol Robotics Laboratory
  3. European Research Council (ERC) [679355] Funding Source: European Research Council (ERC)

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Birds inspire strategies for small unmanned aerial vehicles (SUAVs) to harness complex wind flows in urban environments, with research showing urban nesting gulls making energy savings through soaring during daily commutes and adjusting airspeed to optimize flight trajectory. Proposed models could significantly reduce energy requirements for SUAV platforms by implementing optimized airspeeds based on wind conditions.
Small unmanned aerial vehicles (SUAVs) operating in urban environments must deal with complex wind flows and endurance limitations caused by current battery technology. Birds offer inspiration regarding how to fly in these environments and how to exploit complex wind flows as an energy source. On a broad scale, migrating birds adjust airspeed to minimize cost of transport (COT) in response to wind conditions, but it is unknown whether birds implement these strategies in fine-scale, complex environments. GPS backpacks were used to track 11 urban nesting gulls and found they soared extensively during daily commutes, using thermal and orographic updrafts. This paper outlines COT theory and proposes a model for optimizing airspeed for wind while maintaining flight trajectory. The gull flight paths were tested for COT adjustments, considering their flapping and soaring strategies, and it was found that the birds were able to make energy savings of 31% based on having a best glide speed when soaring that was similar to their minimum power speed when flapping. These models calculated optimum airspeeds based on wind speed and direction and could be implemented on SUAV platforms with wind sensing capabilities. This approach could significantly reduce energy requirements for SUAVs flying in urban environments.

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