A Real-time Algorithm for USV Navigation Based on Deep Reinforcement Learning

Zhiguo Zhou, Yipeng Zheng, Kaiyuan Liu, Xu He, Chong Qu

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

7 Citations (Scopus)

Abstract

Aiming at the demand of flexibility and real-time performance in unknown aquatorium, a path planning algorithm based on Deep Reinforcement Learning (DRL) is proposed. According to plan-avoid-acclimate request, the proposed algorithm involves optimization of net structure and navigation data enrichment based on A3C, re-regulation of action space of the agent, and is trained with specific tasks in three kinds of maps to improve flexibility. The algorithm is integrated with GPU, which helps achieve high training efficiency and real-time performance by creating a neural network to collect pre-training data. Experimental results show that obstacle ability is confirmed. In comparison with current algorithm, training time reduces by 59.3% and efficiency rises by more than 71.7%. Meanwhile, performance of trained model in unknown environment is validated.

Original languageEnglish
Title of host publicationICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123455
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China
Duration: 11 Dec 201913 Dec 2019

Publication series

NameICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/TerritoryChina
CityChongqing
Period11/12/1913/12/19

Keywords

  • deep reinforcement learning
  • flexibility
  • path planning
  • real-time performance
  • unmanned surface vehicle

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