Joint Path and Pick-Up Design for Connectivity-Aware UAV-Enabled Multi-Package Delivery

This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-10-07
DOI
https://doi.org/10.1109/tits.2024.3467329
Citations
8
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
2.79

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article

Joint Path and Pick-Up Design for Connectivity-Aware UAV-Enabled Multi-Package Delivery

Qingqing Wu, Xiaojun Yuan, Bin Duo, Yonghui Li et al.
8 citations
IEEE Transactions on Intelligent Transportation Systems
Advanced Manufacturing and Logistics Optimization
2.79
article

Joint Path and Pick-Up Design for Connectivity-Aware UAV-Enabled Multi-Package Delivery

Qingqing Wu, Xiaojun Yuan, Bin Duo, Yonghui Li, Aoqi Kong
article en
8 citations

Abstract

This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(12)
The University of Sydney (AU), University of Electronic Science and Technology of China (CN), Shanghai Jiao Tong University (CN), Chengdu University of Technology (CN)
Tianjin Science and Technology Program
Affordable and clean energy
Openalex Percentile: Top 9%
Advanced Manufacturing and Logistics Optimization
2.79
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