The Flying Warehouse Delivery System: A Quantitative Approach for the Optimal Operation Policy of Airborne Fulfillment Center

Jeong, Ho Young; Song, Byung Duk; Lee, Seokcheon

doi:10.1109/tits.2020.3003900

Cited by 28 publications

(13 citation statements)

References 12 publications

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“…Amazon AFC-based delivery is intended to ensure that drone deliveries are made within 10 min and can provide services over a wide area. Jeong et al [9] proposed an AFC-based drone delivery system in which an MILP is used to optimize the delivery operation between the AFC and drones.…”

Section: Literature Reviewmentioning

confidence: 99%

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JRCS: Joint Routing and Charging Strategy for Logistics Drones

Arafat

Moh

2022

IEEE Internet Things J.

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Unmanned aerial vehicles (UAVs), commonly known as drones, are currently being used to combat the COVID-19 pandemic through applications, including the delivery of medical supplies, aerial spraying, and public space monitoring. In a pandemic, drone-based delivery is a promising and highly efficient method to reduce transportation time, cost, and exposure to infection. However, owing to both the limited battery lifetime and the limited functions of UAV in-flight missions, it is difficult to implement multiple deliveries over long distances in a single transportation mission. In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization methods are required to design the delivery area networks of customers, charging stations, and delivery routes. We propose a joint routing and charging strategy (JRCS) comprising three phases to perform multiple deliveries in a single mission. We first split the customers of a delivery area using a clustering algorithm according to their distance from the nearest charging station and the maximum flight range. The second phase provides flight segmentation and intersegment routes between the depot, customer locations, and charging stations based on the maximum flight range and safe flight distance. The joint consideration of drone routes with charging stations minimizes the number of charging stations and ensures safe delivery. Finally, we formulate mixed-integer linear programming to solve the drone delivery route problem. According to simulation results, the proposed JRCS outperforms existing delivery approaches in terms of various performance metrics.

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Section: Literature Reviewmentioning

confidence: 99%

“…In particular, drones can provide delivery at a low-cost, on-demand, faster, and without human involvement. Several e-commerce platforms use drones for commercial package delivery, including Amazon, Google, UPS, and DHL [9].…”

Section: Introductionmentioning

confidence: 99%

JRCS: Joint Routing and Charging Strategy for Logistics Drones

Arafat

Moh

2022

IEEE Internet Things J.

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“…Particularly, the IDC must ensure that the payload oscillations are minimum, while tracking a set trajectory. Moreover, the IDC must be capable of providing safety critical decision control in order to avoid dynamic obstacles in the cluttered environment, while maintaining 1 Due to their high dimensionality, the matrices are uploaded as .csv files, and can be viewed here. The structure of IDC ensures powerful versatility in the sense that any tracking controller can be used in conjunction with the obstacle avoidance controller.…”

Section: Integrated Decision Controller Design For Safety-critical Na...mentioning

confidence: 99%

“…This is because utilizing multiple UAVs to lift payloads are economically feasible and efficient, as opposed to utilizing a single UAV of a bigger form factor. Industrial missions [1], military expeditions [2] and medical search-rescue operations [3] demand that the UAVs have to ensure proper delivery of the payload along a set pre-planned global trajectory, while at the same time, preventing collisions with the local dynamic obstacles present around it and maintaining minimal oscillations of the payload throughout the course. However, when there are multiple UAVs interacting with one another to Nishanth Rao is with the Artificial Intelligence and Robotics Lab, Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India, 560012. nishanthrao@iisc.ac.in Suresh Sundaram is with the Artificial Intelligence and Robotics Lab, Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India, 560012. vssuresh@iisc.ac.in transport a payload, existing path planning algorithms [4] fall short, as they don't consider the kinodynamic-maneuverability of the system and it's highly coupled complex dynamics.…”

Section: Introductionmentioning

confidence: 99%

Integrated Decision Control Approach for Cooperative Safety-Critical Payload Transport in a Cluttered Environment

Rao¹,

Sundaram²

2022

Preprint

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In this paper, the problem of coordinated transportation of heavy payload by a team of UAVs in a cluttered environment is addressed. The payload is modeled as a rigid body and is assumed to track a pre-computed global flight trajectory from a start point to a goal point. Due to the presence of local dynamic obstacles in the environment, the UAVs must ensure that there is no collision between the payload and these obstacles while ensuring that the payload oscillations are kept minimum. An Integrated Decision Controller (IDC) is proposed, that integrates the optimal tracking control law given by a centralized Model Predictive Controller with safetycritical constraints provided by the Exponential Control Barrier Functions. The entire payload-UAV system is enclosed by a safe convex hull boundary, and the IDC ensures that no obstacle enters this boundary. To evaluate the performance of the IDC, the results for a numerical simulation as well as a high-fidelity Gazebo simulation are presented. An ablation study is conducted to analyze the robustness of the proposed IDC against practical dubieties like noisy state values, relative obstacle safety margin, and payload mass uncertainty. The results clearly show that the IDC achieves both trajectory tracking and obstacle avoidance successfully while restricting the payload oscillations within a safe limit.

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“…e role of moving depots is to bring the goods and drones closer to the delivery zone, from where the last phase of the delivery is realized only by drones. In the literature, the variants differ mainly in the vehicle type used as mobile depots-GDVs [36], public transportation vehicles (trams) [37,38], barges [39], or even flying warehouses (airships) [40][41][42]. In the case of using GDVs as moving depots, reserved parking spots are required for drone launching, while, in the case of combining drones with public transportation vehicles, barges, or floating warehouses, the delivery is realized without the need for mobile depot stopping.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Spherical Fuzzy MARCOS Method for Assessment of Drone‐Based City Logistics Concepts

et al. 2021

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Technological innovations from the last few years, in the combination with city logistics (CL) initiatives, make the definition of novel, complex, sustainable CL solutions possible. Unmanned aerial vehicles (drones) as a technology attracted lots of attention in the literature. Various researches focused on different drone-based delivery approaches, but there are only a few articles dealing with drones as the elements of complex CL concepts. The goal of this paper is to evaluate different drone-based CL concepts. Based on the existing ideas of drone application in delivery, the main group of CL concepts and their variants are defined, which represents the main contribution of the article. The evaluation and ranking of concepts are performed from the aspect of all CL stakeholders and the defined set of criteria by applying measurement of alternatives and ranking according to compromise solution (MARCOS) multicriteria decision-making (MCDM) method in spherical fuzzy environment, which represents another contribution of the article. The results indicate that the potentially best CL concept, with the final score of 0.408, is the one that refers to the transformation of the logistics system into a two-echelon system with the implementation of micro-consolidation centers (MCCs), in which the delivery of goods to MCCs is realized with rail transportation mode and the last delivery phase with drones. It is followed by the concepts that imply MCCs, and rail transportation in the function of mobile depots for drone launching and MCCs and ground delivery vehicles (GDVs), with the final scores of 0.395 and 0.390, respectively.

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The Flying Warehouse Delivery System: A Quantitative Approach for the Optimal Operation Policy of Airborne Fulfillment Center

Cited by 28 publications

References 12 publications

JRCS: Joint Routing and Charging Strategy for Logistics Drones

JRCS: Joint Routing and Charging Strategy for Logistics Drones

Integrated Decision Control Approach for Cooperative Safety-Critical Payload Transport in a Cluttered Environment

Novel Spherical Fuzzy MARCOS Method for Assessment of Drone‐Based City Logistics Concepts

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