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Persistent UAV service: An improved scheduling formulation and prototypes of system components

  • Byung Duk Song
  • , Jonghoe Kim
  • , Jeongwoon Kim
  • , Hyorin Park
  • , James R. Morrison
  • , David Hyunchul Shim

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

The flight duration of unmanned aerial vehicles (UAVs) is limited by their battery or fuel capacity. As a consequence, the duration of missions that can be pursued by UAVs without supporting logistics is restricted. However, a system of UAVs that is supported by automated logistics structures, such as fuel service stations and orchestration algorithms, may pursue missions of conceivably indefinite duration. This may be accomplished by handing off the mission tasks to fully fueled replacement UAVs when the current fleet grows weary. The drained UAVs then seek replenishment from nearby logistics support facilities. To support the vision of a persistent fleet of UAVs pursuing missions across a field of operations, we develop an improved mixed integer linear programming (MILP) model that can serve to support the system's efforts to orchestrate the operations of numerous UAVs, missions and logistics facilities. Further, we look toward the future implementation of such a persistent fleet outdoors and develop prototype components required for such a system. In particular, we develop and demonstrate the concerted operation of a scheduling model, UAV onboard vision-based guidance system and replenishment stations.

Original languageEnglish
Pages (from-to)221-232
Number of pages12
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume74
Issue number1-2
DOIs
Publication statusPublished - Apr 2014

Bibliographical note

Funding Information:
The work reported here has been supported in part by KAIST HRHRP grant N10120008 and by the National Research Foundation of Korea (NRF) Basic Science Research Program grant 2013012292.

Keywords

  • Persistent UAV service
  • Replenishment stations
  • Scheduling for persistence
  • Vision-based guidance systems

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