Depth Control of a Biomimetic Manta Robot via Reinforcement Learning

Daili Zhang, Guang Pan, Yonghui Cao, Qiaogao Huang, Yong Cao

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

1 Scopus citations

Abstract

This paper proposes a model-free biomimetic manta robot depth control method based on reinforcement learning. Different from the traditional control method, the reinforcement learning method does not need to establish a mathematical model of the control object, and autonomously learns the control law through data training. Based on the classical Q algorithm, the state space, the action space, and reward function of the depth control of the bionic manta robot are designed. The state-action function is trained offline using the experience replay mechanism and random sampling strategy. Finally, the trained function is transplanted to the biomimetic manta robot prototype to establish a controller. The effectiveness of the proposed control method is verified by experiments.

Original languageEnglish
Title of host publicationCognitive Systems and Information Processing - 7th International Conference, ICCSIP 2022, Revised Selected Papers
EditorsFuchun Sun, Angelo Cangelosi, Jianwei Zhang, Yuanlong Yu, Huaping Liu, Bin Fang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages59-69
Number of pages11
ISBN (Print)9789819906161
DOIs
StatePublished - 2023
Event7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022 - Fuzhou, China
Duration: 17 Dec 202218 Dec 2022

Publication series

NameCommunications in Computer and Information Science
Volume1787 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022
Country/TerritoryChina
CityFuzhou
Period17/12/2218/12/22

Keywords

  • Autonomous underwater vehicle
  • Biomimetic manta robot
  • Depth control
  • Reinforcement learning

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