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A Comparative Review of Air Drones (UAVs) and Delivery Bots (SUGVs) for Automated Last Mile Home Delivery

Author

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  • Fang Li

    (Institute for Logistics, Risk- and Resource Management (ILR), Neu-Ulm University of Applied Sciences, Wileystraße 1, 89231 Neu-Ulm, Germany)

  • Oliver Kunze

    (Institute for Logistics, Risk- and Resource Management (ILR), Neu-Ulm University of Applied Sciences, Wileystraße 1, 89231 Neu-Ulm, Germany)

Abstract

Background : UAVs (Unmanned Aerial Vehicles) and SUGVs (Sidewalk Unmanned Ground Vehicles) are two prominent options to revolutionize last mile home delivery. However, there is no literature yet addressing a comprehensive assessment of them. To bridge this research gap, this paper aimed to compare UAVs to SUGVs in the context of urban parcel delivery from a practical, conceptual, technological, commercial, and environmental perspective. Methodology : Based on structured literature and web research, this paper provided a comparative status quo review of these two delivery concepts. We introduced a parameter-based cost calculus model to estimate the costs per shipment for each technology. To detect the key cost drivers, we applied a one-way sensitivity analysis, as well as a “full factorial design of experiment” approach. Results : These key cost drivers for both operations are the “number of vehicles per operator” and the “average beeline service radius”. From today’s commercial point of view, our model indicated better profitability of SUGVs. However, technical and regulatory developments may render different results in the future. As SUGVs emit significantly less noise than UAVs, we assume that SUGVs have an additional advantage for usage in autonomous urban last mile delivery from a resident’s perspective. Conclusions : Both key cost drivers will significantly influence the commercial viability of unmanned home delivery services. Safety and security aspects will determine regulatory rules on “number of vehicles per operator”. To increase the “average beeline service radius”, UAVs could profit from mothership delivery concepts while SUGV delivery may co-use existing public transport infrastructure.

Suggested Citation

  • Fang Li & Oliver Kunze, 2023. "A Comparative Review of Air Drones (UAVs) and Delivery Bots (SUGVs) for Automated Last Mile Home Delivery," Logistics, MDPI, vol. 7(2), pages 1-32, April.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:2:p:21-:d:1114390
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    References listed on IDEAS

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    1. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    2. Asma Troudi & Sid-Ali Addouche & Sofiene Dellagi & Abderrahman El Mhamedi, 2018. "Sizing of the Drone Delivery Fleet Considering Energy Autonomy," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
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    Citations

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    Cited by:

    1. Marc Guerreiro Augusto & Julian Maas & Martin Kosch & Manuel Henke & Tobias Küster & Frank Straube & Sahin Albayrak, 2025. "Autonomous Van and Robot Last-Mile Logistics Platform: A Reference Architecture and Proof of Concept Implementation," Logistics, MDPI, vol. 9(1), pages 1-15, January.
    2. Bahram Alidaee & Haibo Wang & Lutfu S. Sua, 2023. "The Last-Mile Delivery of Heavy, Bulky, Oversized Products: Literature Review and Research Agenda," Logistics, MDPI, vol. 7(4), pages 1-16, December.
    3. Pannee Suanpang & Pitchaya Jamjuntr, 2024. "Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development," Sustainability, MDPI, vol. 16(17), pages 1-36, September.
    4. Maren Schnieder, 2024. "Visualising Carrier Consolidation and Alternative Delivery Locations: A Digital Model of Last-Mile Delivery in England and Wales," Logistics, MDPI, vol. 8(3), pages 1-14, August.
    5. Noel Stierlin & Martin Risch & Lorenz Risch, 2024. "Current Advancements in Drone Technology for Medical Sample Transportation," Logistics, MDPI, vol. 8(4), pages 1-26, October.
    6. Roberto Montemanni & Derek H. Smith, 2024. "A Compact Model for the Clustered Orienteering Problem," Logistics, MDPI, vol. 8(2), pages 1-15, May.

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