Rodrigues Thiago A, Patrikar Jay, Oliveira Natalia L, Matthews H Scott, Scherer Sebastian, Samaras Constantine
Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
Patterns (N Y). 2022 Aug 5;3(8):100569. doi: 10.1016/j.patter.2022.100569. eCollection 2022 Aug 12.
Uncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of COe per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery.
用于最后一英里配送的无人机将影响配送的能源生产率，需要新的方法来了解能源消耗和温室气体（GHG）排放。我们将188次不同速度的四旋翼飞行的实证测试与第一性原理分析相结合，以开发一个可用的能源模型和一种机器学习算法，来评估起飞、巡航和着陆过程中的能源消耗。我们的模型表明，在美国，搭载非常小包裹（0.5千克）的电动四旋翼无人机每公里将消耗约0.08兆焦耳的能量，每个包裹会产生70克二氧化碳当量。我们将无人机配送与其他车辆进行比较，结果显示，无人机每配送一个包裹所消耗的能量（0.33兆焦耳/包裹）可比传统运输方式低94%，只有电动货运自行车的单位包裹温室气体排放量更低。我们的开放模型和系数可帮助利益相关者理解并提高小包裹配送的可持续性。
