Reliable path planning for drone delivery using a stochastic time-dependent public transportation network

Hailong Huang, Andrey V. Savkin, Chao Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9058989
Pages (from-to)4941-4950
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number8
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Keywords

  • Parcel delivery
  • drones
  • path planning
  • public transportation network
  • stochastic time-dependent network
  • unmanned aerial vehicles (UAVs)

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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