Multi-Objective Optimization for Drone Delivery

Sawadsitang, Suttinee; Niyato, Dusit; Tan, Puay Siew; Wang, Ping; Nutanong, Sarana

doi:10.1109/vtcfall.2019.8891117

Cited by 21 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent advances in the drone (unmanned aerial vehicles, or UAV) technology allow one to perform unmanned delivery-to-the-door of various goods [19]. Drones succeed with blood transportation [20][21][22][23][24] (and even organ transportation is being explored [25]), implying that transporting by drones the less sensitive [12] COVID-19 test samples (e.g, upper respiratory specimens in nasopharyngeal and oropharyngeal swab or lower respiratory specimens like sputum) may also be technically feasible [26] (related studies went so far as to explore testing for avian influenza A (H7N9) virus directly on the drone [27]).…”

Section: Resultsmentioning

confidence: 99%

Modeling quarantine during epidemics and mass-testing using drones

2020

View full text Add to dashboard Cite

We extend the classical SIR epidemic spread model by introducing the "quarantined" compartment. We solve (numerically) the differential equations that govern the extended model and quantify how quarantining "flattens the curve" for the proportion of infected population over time. Furthermore, we explore the potential of using drones to deliver tests, enabling mass-testing for the infection; we give a method to estimate the drone fleet needed to deliver the tests in a metropolitan area. Application of our models to COVID-19 spread in Sweden shows how the proposed methods could substantially decrease the peak number of infected people, almost without increasing the duration of the epidemic.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling quarantine during epidemics and mass-testing using drones

2020

View full text Add to dashboard Cite

show abstract

“…Recent advances in the drone (unmanned aerial vehicles, or UAV) technology allow one to perform unmanned delivery-to-the-door of various goods [SNTWN19]. Drones succeed with blood transportation [AHSW+17, ANTBS17, ASPHS15, GRPS+14, KCMG+18] (and even organ transportation is being explored [SRSBBW18]), implying that transporting by drones the less sensitive [WHO20] COVID-19 test samples (e.g, upper respiratory specimens in nasopharyngeal and oropharyngeal swab or lower respiratory specimens like sputum) may also be technically feasible [ASCMZ16] (related studies went so far as to explore testing for avian influenza A (H7N9) virus directly on the drone [WF06]).…”

Section: Resultsmentioning

confidence: 99%

Modeling quarantine during epidemics and mass-testing using drones

Sedov

Красночуб

Polishchuk

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, a German international express delivery company, DHL, is working on a project to deliver drugs with drones. In addition, Alibaba in China is developing drones for logistics delivery and plans to use them in mountainous and island areas in the future [ 3 , 4 ]. Furthermore, many UAVs are being deployed in mission-critical services, such as tracking wide disaster sites and delivering emergency kits for rescue mission scenarios [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Fisheye-Based Smart Control System for Autonomous UAV Operation

Han¹

2020

Sensors

View full text Add to dashboard Cite

Recently, as UAVs (unmanned aerial vehicles) have become smaller and higher-performance, they play a very important role in the Internet of Things (IoT). Especially, UAVs are currently used not only in military fields but also in various private sectors such as IT, agriculture, logistics, construction, etc. The range is further expected to increase. Drone-related techniques need to evolve along with this change. In particular, there is a need for the development of an autonomous system in which a drone can determine and accomplish its mission even in the absence of remote control from a GCS (Ground Control Station). Responding to such requirements, there have been various studies and algorithms developed for autonomous flight systems. Especially, many ML-based (Machine-Learning-based) methods have been proposed for autonomous path finding. Unlike other studies, the proposed mechanism could enable autonomous drone path finding over a large target area without size limitations, one of the challenges of ML-based autonomous flight or driving in the real world. Specifically, we devised Multi-Layer HVIN (Hierarchical VIN) methods that increase the area applicable to autonomous flight by overlaying multiple layers. To further improve this, we developed Fisheye HVIN, which applied an adaptive map compression ratio according to the drone’s location. We also built an autonomous flight training and verification platform. Through the proposed simulation platform, it is possible to train ML-based path planning algorithms in a realistic environment that takes into account the physical characteristics of UAV movements.

show abstract

Multi-Objective Optimization for Drone Delivery

Cited by 21 publications

References 9 publications

Modeling quarantine during epidemics and mass-testing using drones

Modeling quarantine during epidemics and mass-testing using drones

Modeling quarantine during epidemics and mass-testing using drones

Fisheye-Based Smart Control System for Autonomous UAV Operation

Contact Info

Product

Resources

About