DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition-Reference-Cited by-同舟云学术

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DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 Workshop on Robotics in Education (WRE)
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Author:

Grando Ricardo B.¹,De Jesus Junior C.²,Kich Victor A.³,Kolling Alisson H.³,Mateus Matheus G.²,Guerra Rodrigo S.²,Drews Paulo L. J.²

Affiliation:

1. Technological University of Uruguay,Robotics Lab,Rivera,Uruguay

2. Centro de Ciências Computacionais Universidade Federal de Rio Grande,Rio Grande,Brazil

3. Universidade Federal de Santa Maria Universidade Federal de Santa Maria (UFSM),Santa Maria,Brazil

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10332459/10332917/10333008.pdf?arnumber=10333008

Reference40 articles.

1. Hybrid Unmanned Aerial Underwater Vehicle: Modeling and simulation

2. Attitude control for an Hybrid Unmanned Aerial Underwater Vehicle: A robust switched strategy with global stability

3. A Comparative Study on Sigma-Point Kalman Filters for Trajectory Estimation of Hybrid Aerial-Aquatic Vehicles

4. Design and implementation of multirotor aerial-underwater vehicles with experimental results

5. A Multimodal Aerial Underwater Vehicle with Extended Endurance and Capabilities

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