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.

在城市环境中运行的小型无人机 (SUAV) 必须应对当前电池技术造成的复杂风流和续航限制。鸟类提供了有关如何在这些环境中飞行以及如何利用复杂的风流作为能源的灵感。在广泛的范围内，候鸟会根据风力条件调整空速以最小化运输成本 (COT)，但尚不清楚鸟类是否在精细、复杂的环境中实施这些策略。GPS 背包被用来追踪 11 只城市筑巢海鸥，发现它们在日常通勤中利用热量和地形上升气流大幅飙升。本文概述了 COT 理论，并提出了一种在保持飞行轨迹的同时优化风速的模型。对海鸥飞行路径进行了 COT 调整测试，考虑到他们的扑翼和翱翔策略，我们发现，基于与扑翼时的最小功率速度相似的最佳翱翔速度，鸟类能够节省 31% 的能源。这些模型根据风速和风向计算最佳空速，并且可以在具有风感功能的 SUAV 平台上实施。这种方法可以显着降低在城市环境中飞行的 SUAV 的能源需求。