Online Informative Path Planning for Autonomous Underwater Vehicles with Cross Entropy Optimization

Yang Li, Rongxin Cui, Demin Xu, Shuqiang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Autonomous underwater vehicles (AUVs) have been extensively utilized both in civil and military applications, among which the marine environment monitoring is one of the key issue. In this paper, we focus on online informative path planning for long-term monitoring in continuous workspace. We point out that the likelihood of measurements is related to when it is acquired. Thus we first model the underwater environment based on modified Gaussian process (GP) with considering the dynamic likelihood of measurements. Then, clamped B-curve is utilized to parametrize the continuous path segments. In order to maximize the amount of received information, we propose a path replanning scheme based on cross-entropy optimization. Moreover, we introduce the numerical simulation to highlight the effectiveness of our algorithm.

Original languageEnglish
Title of host publicationICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages566-571
Number of pages6
ISBN (Electronic)9781538670668
DOIs
StatePublished - 11 Jan 2019
Event3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018 - Singapore, Singapore
Duration: 18 Jul 201820 Jul 2018

Publication series

NameICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics

Conference

Conference3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
Country/TerritorySingapore
CitySingapore
Period18/07/1820/07/18

Keywords

  • Cross entropy optimization
  • Gaussian process
  • Informative path planning

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