TY - JOUR
T1 - Reliable path planning for drone delivery using a stochastic time-dependent public transportation network
AU - Huang, Hailong
AU - Savkin, Andrey V.
AU - Huang, Chao
N1 - Funding Information:
Manuscript received July 16, 2019; revised November 17, 2019 and January 26, 2020; accepted March 23, 2020. Date of publication April 7, 2020; date of current version August 9, 2021. This work was supported by Australian Research Council. The Associate Editor for this article was M. Menendez. (Corresponding author: Hailong Huang.) Hailong Huang and Andrey V. Savkin are with the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Drones have been regarded as a promising means for future delivery industry by many logistics companies. Several drone-based delivery systems have been proposed but they generally have a drawback in delivering customers locating far from warehouses. This paper proposes an alternative system based on a public transportation network. This system has the merit of enlarging the delivery range. As the public transportation network is actually a stochastic time-dependent network, we focus on the reliable drone path planning problem (RDPP). We present a stochastic model to characterize the path traversal time and develop a label setting algorithm to construct the reliable drone path. Furthermore, we consider the limited battery lifetime of the drone to determine whether a path is feasible, and we account this as a constraint in the optimization model. To accommodate the feasibility, the developed label setting algorithm is extended by adding a simple operation. The complexity of the developed algorithm is analyzed and how it works is demonstrated via a case study.
AB - Drones have been regarded as a promising means for future delivery industry by many logistics companies. Several drone-based delivery systems have been proposed but they generally have a drawback in delivering customers locating far from warehouses. This paper proposes an alternative system based on a public transportation network. This system has the merit of enlarging the delivery range. As the public transportation network is actually a stochastic time-dependent network, we focus on the reliable drone path planning problem (RDPP). We present a stochastic model to characterize the path traversal time and develop a label setting algorithm to construct the reliable drone path. Furthermore, we consider the limited battery lifetime of the drone to determine whether a path is feasible, and we account this as a constraint in the optimization model. To accommodate the feasibility, the developed label setting algorithm is extended by adding a simple operation. The complexity of the developed algorithm is analyzed and how it works is demonstrated via a case study.
KW - Parcel delivery
KW - drones
KW - path planning
KW - public transportation network
KW - stochastic time-dependent network
KW - unmanned aerial vehicles (UAVs)
UR - http://www.scopus.com/inward/record.url?scp=85083080363&partnerID=8YFLogxK
U2 - 10.1109/TITS.2020.2983491
DO - 10.1109/TITS.2020.2983491
M3 - Journal article
SN - 1524-9050
VL - 22
SP - 4941
EP - 4950
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 8
M1 - 9058989
ER -