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Delivery drones are being widely developed as a potential way to deliver packages, but their rapidly developing technology and lack of precedent pose challenges to understanding the potential societal impacts. As policymakers sort out their available policy levers, simple city-scale models can help provide a preliminary understanding of the issues....
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... For background and applications of CA models, see Ansari et al. [28], Daganzo [29,30], Franceschetti et al. [31], and Langevin et al. [32]. There are a few studies investigate the strategic aspects of DDP (e.g., [33,34]), TSP-D (e.g., [35]), and VRP-D (e. g., [12,36,37]) using continuous approximation methods. Figliozzi [33], Lohn [34], and Goodchild and Toy [38] compare the environmental benefits of drone-only delivery versus conventional truck delivery and suggest that a blended system would perform the best with drones serving closer customers while trucks serve more remote ones. ...
... There are a few studies investigate the strategic aspects of DDP (e.g., [33,34]), TSP-D (e.g., [35]), and VRP-D (e. g., [12,36,37]) using continuous approximation methods. Figliozzi [33], Lohn [34], and Goodchild and Toy [38] compare the environmental benefits of drone-only delivery versus conventional truck delivery and suggest that a blended system would perform the best with drones serving closer customers while trucks serve more remote ones. In particular, Figliozzi [33] shows that trucks are more environmentally friendly with a number of customers (e.g., 50 or more). ...
... In particular, Figliozzi [33] shows that trucks are more environmentally friendly with a number of customers (e.g., 50 or more). Lohn [34] finds that by increasing the percentage of customers delivered by drones can increase the energy consumed substantially due to the back and forth flying distance by drones. Although the author suggests increasing the number of drone stations, our research proposes an integrated delivery system with a combination of drone-only, truck-drone, and truck-only. ...
Home package delivery by drones as an alternative or complement to traditional delivery by trucks is attracting considerable attention from the commercial sector as well as academia. While drone delivery may offer considerable benefits, the fundamental issues of how best to deploy drones for small package delivery are still not well understood. Unlike most studies in the literature that consider either truck-drone tandems or drone standalone delivery operations, our research provides a strategic analysis for the optimal design of an integrated drone delivery system in which customers in a service region can be served by a combination of truck-drone (TD) delivery, drone-only (DO) delivery, and truck-only (TO) delivery. We formulate and optimize continuous approximation models to identify the optimal delivery service combination that minimizes the total delivery costs including truck and drone operating and stop costs. We evaluate the impact of a wide range of operating characteristics and delivery environments on the optimal delivery system and the benefits of using drones. Our results suggest that an optimal delivery system design often uses a mix of DO and TD, but TD is the dominant delivery service in most scenarios. The magnitude of the cost savings afforded by the use of drones, relative to truck-only delivery, can be large but strongly depends on the values of key parameters.
Access to the article: https://authors.elsevier.com/a/1iitM15N2O-SfN
... D'Andrea (2014) integrated a model that is also used in a series of reports from the RAND Corporation. The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017;Xu 2017;Gulden 2017). Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. ...
... The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017;Xu 2017;Gulden 2017). Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. Xu (2017) discussed aspects of drone design related to drone energy consumption and suggested that fixed-wing VTOL (vertical takeoff and landing) or hybrid multicopter configurations that combine VTOL capabilities with lifting surfaces (wing-like structures) would be more suitable for many drone delivery purposes. ...
... More recently, Amazon [7] has planned to use UAVs for 30-min home deliveries and is conducting an alpha test on a small village in England, with the first delivery to customers via drone becoming a reality. The topic of last-mile logistics is a topic explored by various authors, ranging through the many facets of the segment: from problems in terms of energy consumption and social and environmental impact [8] to models for the optimal routing and scheduling of UAVs and delivery trucks, including in collaboration with each other [9]. More specifically, in recent years, numerous authors have dealt with the problem of last-mile deliveries from the point of view of transport planning. ...
Drone delivery has gained increasing importance in the past few years. Recent technology advancements have allowed us to think of systems capable of transporting and delivering goods precisely and efficiently. However, in order to switch from a test environment to a real environment, many open issues need to be addressed. In this paper, we focused on drop-off point localization based on fiducial markers, analyzing different systems and the configuration of different aspects. We tested our system in a real-world environment and drew conclusions which lead us to identify the most reliable fiducial system and family for this use case.
... D'Andrea (2014) integrated a model that is also used in a series of reports from the RAND Corporation. The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017;Xu 2017;Gulden 2017). Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. ...
... The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017;Xu 2017;Gulden 2017). Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. Xu (2017) discussed aspects of drone design related to drone energy consumption and suggested that fixed-wing VTOL (vertical takeoff and landing) or hybrid multicopter configurations that combine VTOL capabilities with lifting surfaces (wing-like structures) would be more suitable for many drone delivery purposes. ...
At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV’s energy consumption as well as an investigation of different energy models.
... However, such testing has been limited to rural areas; dense urban areas were excluded as there are various regulations concerning drone mass, population density, altitude, and use, although cases varied substantially from country to country [1][2][3][4][5]. In addition, UAV delivery services are not yet suitable for precise urban deliveries in terms of safety, energy consumption, infrastructure requirements, aerial congestion, privacy, and noise for a city [6][7][8]. Due to the various issues raised, the potential of UAV delivery services has not been witnessed in urban areas but rather in rural areas with poor infrastructure of isolated populations [9]. ...
This study examines UAV (unmanned aerial vehicle) delivery services using metropolitan subway tracks in South Korea. The aim of the study is to enhance the usefulness of UAV delivery services in urban areas, evaluating what kinds of UAVs are more environment friendly than freight trains, with regard to particulate matter emissions and global warming potential. Under evaluation conditions, freight train delivery was a significantly better alternative in terms of particulate matter emissions, regardless of the size and energy source of the UAVs. However, despite freight trains being a well-known eco-friendly mode of transportation, it can be seen from this study that small UAVs satisfied a few conditions that could potentially provide a good transportation alternative, with low global warming potential. This paper provides important insights into the comparison of UAVs and freight trains with regard to carbon and particulate matter emissions, highlighting the implications that, in some situations, UAVs can be a feasible alternative for policymakers who prepare policy measures of an activation plan for UAM (urban air mobility).
... D'Andrea (2014) integrated a model that is also used in a series of reports from the RAND Corporation. The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017, Xu 2017, Gulden 2017. Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. ...
... The authors explored energy consumption for city-scale drone delivery systems (Lohn 2017, Xu 2017, Gulden 2017. Lohn (2017) used this model to analyze truck and drone delivery in cities of various sizes, and Gulden (2017) provided a GIS-based analysis of shifting truck deliveries to drones in Minneapolis. Xu (2017) discussed aspects of drone design related to drone energy consumption and suggested that fixed-wing VTOL (vertical takeoff and landing) or hybrid multicopter configurations that combine VTOL capabilities with lifting surfaces (wing-like structures) would be more suitable for many drone delivery purposes. ...
Today, there are increasing demands for flying drones with diverse capabilities for civilian and military uses, and there is growing attention given to this topic. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to the nature of the problem, it appears that it is necessary to identify the factors affecting the energy consumption of UAVs during the execution of missions as well as examine the general factors that influence the consumption of energy. The purpose of this chapter is to provide an overview of the current state of research in the area of UAV energy consumption. This is followed by general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models.
... The external conditions in which our infrastructure need to function are changing rapidly. Acceleration of climate change (IPCC 2014), acceleration of the integration of cybertechnologies (and with that security concerns) (Chester and Allenby 2020a), hyperpolarization that routinely stifles innovative reform (Pildes 2011), distributed control (with third parties increasingly driving service consumption, e.g., phone navigation software and smart home energy technologies) (Poska et al 2021), shifting priorities (e.g., pushes to increase renewables) (The Economist 2021), and new competition (Amazon drone delivery as infrastructure) (Lohn 2017), to name a few, represent dramatic shifts in the environments in which legacy infrastructure need to remain viable. We use the term environment in the broadest of senses including technological, political, social, cultural in addition to environmental change. ...
Infrastructure systems must change to match the growing complexity of the environments they operate in. Yet the models of governance and the core technologies they rely on are structured around models of relative long-term stability that appear increasingly insufficient and even problematic. As the environments in which infrastructure function become more complex, infrastructure systems must adapt to develop a repertoire of responses sufficient to respond to the increasing variety of conditions and challenges. Whereas in the past infrastructure leadership and system design has emphasized organization strategies that primarily focus on exploitation (e.g., efficiency and production, amenable to conditions of stability), in the future they must create space for exploration, the innovation of what the organization is and does. They will need to create the abilities to maintain themselves in the face of growing complexity by creating the knowledge, processes, and technologies necessary to engage environment complexity. We refer to this capacity as infrastructure autopoiesis. In doing so infrastructure organizations should focus on four key tenets. First, a shift to sustained adaptation – perpetual change in the face of destabilizing conditions often marked by uncertainty – and away from rigid processes and technologies is necessary. Second, infrastructure organizations should pursue restructuring their bureaucracies to distribute more resources and decisionmaking capacity horizontally, across the organization’s hierarchy. Third, they should build capacity for horizon scanning, the process of systematically searching the environment for opportunities and threats. Fourth, they should emphasize loose fit design, the flexibility of assets to pivot function as the environment changes. The inability to engage with complexity can be expected to result in a decoupling between what our infrastructure systems can do and what we need them to do, and autopoietic capabilities may help close this gap by creating the conditions for a sufficient repertoire to emerge.
... Figliozzi (2017) evaluated emissions of CO2 by comparing use of vehicles and manufacture and disposal between UAV and ground vehicles indicating that drones are more suitable for delivering small, light-weight products to meet the requirements of the green supply chain (Welser IV & Xu, 2016). Lohn (2017) argued in the case of drone supplies that the associated UAV infrastructure should be constructed, and noisy drones could undermine drone supply efforts. In the meantime, some technical issues should be resolved, such as job selection for UAVs (Grippa et al., 2016). ...
With the growing global population, the demand for e-commerce business have substantially increased in the last decade. This has required business leaders to think out of the box and to acquire the customers perspective. Urbanisation also captivated people to heading their livelihood from village to cities, which leads to severe congestion in urban areas. This issue is directly impacting the logistics providers to supply their deliveries in urban areas, resulting in unexpected delays in delivering customers. According to a report by the U.N. (2014), states that about 54% of the population live in the cities today, and about 66% is expected in 2050. It seems a massive problem of the cloud is about to cover over the sky, and this is the best time to think meticulously and to take preventive measures for the future. The surge of Industry 4.0 brings numerous technologies along with and the digitalisation is one of them. To provide seamless Last-mile-delivery, adoption of digitalisation is crucial, as Last-mile-delivery is considered as the essential activity of logistics providers. Therefore, the Last-mile-delivery (LMD) by autonomous vehicles could bring a significant transformation in the Supply Chain. Though many organisations have started LMD by autonomous vehicles, still it is waiting to be used widely.
... While most remote drone navigation interfaces follow a one-user-one-robot paradigm, having one visitor control one drone may result in a large crowd of drones at popular tourism sites. Alarms have been raised about the energy waste of an abundance of drones [14,31] and the associated safety [3,39] and privacy [54] risks for local communities. A mechanism for multiple users to share the control of a drone can potentially mitigate these concerns and make remote drone touring more friendly to the environment and local communities. ...
... Multirotor vehicles have been increasingly applied in the private sector for various purposes such as photography, delivery, and transportation frameworks such as UAMs. However, in the proximity of residential areas, the noise of multirotor operations is a major concern [1,2]. In general, the community acceptance and autonomy are crucial aspects to realize the successful implementation of UAVs in cities, and the noise is a key parameter influencing the community acceptance. ...
Multirotor drone generally controls its attitude and position through the variation of the angular speed of each rotor, which makes the sound signature from it considerably different from the one of single-rotor flight vehicle. Specifically, the sound spectrum of single-rotor vehicles is dominated by the blade passing frequency (BPF) components, determined by the angular speed of the rotor. In contrast, the variation in the angular speed in the multirotor makes it challenging to predict the effects of the multirotor noise. In this study, the effects of the variation in the angular speed of each rotor on the noise of a hovering multirotor were quantified and predicted through uncertainty quantification and single-rotor stochastic analysis. Multirotor experimental results indicated that the Revolution per second (RPS) uncertainty of the multirotor is sensitive to maximum tilt angle and payload. The experimentally quantified angular speed variation is used as input to single-rotor stochastic analysis, and the measured aerodynamic and acoustics results show good agreement with the analytically/numerically predicted results. On comparing multirotor noise, the single-rotor noise with RPS uncertainty results exhibit similar spectral characteristics, especially near the BPF harmonics. Furthermore, the results reveal that the single-rotor stochastic simulation with angular speed uncertainty can predict angular speed variation effects on the multirotor noise in the hovering condition. This study concludes that the sound characteristics of a hovering multirotor can be predicted by considering a single-rotor case with RPS uncertainty.
