To better understand the short-term technical viability and future prospects of delivery drones, we developed and exercised a delivery drone performance model. The model is sensitive to key vehicle design parameters of delivery drones, such as aerodynamic, structural, and propulsive efficiencies; battery density; and mission and operating assumptions.

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Design Perspectives on Delivery Drones

by Jia Xu

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Research Questions

  1. What requirements drive delivery drone design?
  2. How can we measure the short-term technical viability of delivery drones?
  3. What does the future of delivery drone performance look like?

As the demand for commercial deliveries increases within cities, companies face a fundamental limitation in surface road capacity. Drone delivery aims to overcome that limitation by exploiting the vertical dimension above city streets. This report explores the vehicle design aspects of the delivery drone problem, including flight efficiency, energy consumption, noise, and safety, which are central to the viability of delivery drones. Importantly, such design aspects also speak to the potential scalability of the concept.

A brief analysis of the requirements shows that a 10- to 15-mile delivery radius is likely sufficient to cover most U.S. urban areas. A vertical takeoff and landing (VTOL) delivery drone can ease operations at the terminal area at a cost-to-flight efficiency. The limited delivery range and payload requirements, coupled with the power efficiency of electric motors at small scales, makes VTOL designs viable.

To better understand the short-term technical viability and future prospects of delivery drones, we developed and tested a simple delivery drone performance model. The model takes in a host of vehicle and mission parameters and assumptions, chief among which are the aerodynamic, structural, and propulsive efficiencies, and the battery energy density. The model is high level, and the parameters are based on analogous systems.

Key Findings

Google Project Wing– and Amazon Prime Air–type delivery drones represent the current limit of what is possible for the baseline design.

  • We assumed a simple, moderately efficient hybrid multicopter configuration equipped with contemporary lithium-ion batteries.
  • For the baseline design, we estimated the energy to deliver a 5-lb payload out to a radius of ten miles to be about 1.5 kilowatt hours.

An advanced design enables more gains through aerodynamic refinements and improved batteries.

  • The combination of a higher lift-to-drag ratio (L/D), denser batteries, improved structural efficiencies, and blended aerodynamic and propulsive control could dramatically reduce energy consumption, leading to five- to seven-fold improvements in flight efficiency.
  • Improved flight efficiency could support multi-stop delivery drones, which could be more efficient for dense delivery demands.

Recommendations

  • More gains are possible with aerodynamic refinements and improved batteries. The combination of higher L/Ds, denser lithium-ion batteries, entirely different battery chemistries (lithium-air, for example), improved structural efficiencies, and blended aerodynamic and propulsive control could dramatically reduce energy consumption, leading to five- to sevenfold improvements in flight efficiency.
  • Improved flight efficiency could support multi-stop delivery drones, which could be more efficient for dense delivery demands.
  • While important aspects of small electric unmanned aerial vehicle aerodynamics, aeroacoustics, and flight control are underresearched in relation to more-established commercial and general aviation aircraft, as the market grows for higher-performance delivery and other commercial systems, we can expect a reorientation of aerospace engineering research and development, as well as education to answer this important and growing research question.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Requirements That Drive Drone Delivery

  • Chapter Three

    Vehicle Configuration

  • Chapter Four

    Performance and Outlook

  • Chapter Five

    Conclusions

  • Appendix

    Summary of Modeling Assumptions

This project is a RAND Venture. Funding was provided by gifts from RAND supporters and income from operations. The research was conducted by the Science, Technology, and Policy Program within RAND Justice, Infrastructure, and Environment.

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