Learning how to avoid obstacles: A numerical investigation for maneuvering of self‐propelled fish based on deep reinforcement learning-Reference-Cited by-同舟云学术

Learning how to avoid obstacles: A numerical investigation for maneuvering of self‐propelled fish based on deep reinforcement learning

Published:2021-07-10 Issue:10 Volume:93 Page:3073-3091
ISSN:0271-2091
Container-title:International Journal for Numerical Methods in Fluids
language:en
Short-container-title:Int J Numer Meth Fluids

Author:

Yan Lang¹,Chang Xinghua²,Wang Nianhua³,Tian Runyu⁴,Zhang Laiping²^ORCID,Liu Wei¹

Affiliation:

1. College of Aerospace Science and Engineering National University of Defense Technology Changsha China

2. Unmanned Systems Research Center National Innovation Institute of Defense Technology Beijing China

3. State Key Laboratory of Aerodynamics China Aerodynamics Research and Development Center Mianyang China

4. Hypervelocity Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Peking University

Publisher

Wiley

Subject

Applied Mathematics,Computer Science Applications,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/fld.5025

Reference37 articles.

1. Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

2. Biological inspiration: From carangiform fish to multi-joint robotic fish

3. Obstacle effects on electrocommunication with applications to object detection of underwater robots

4. A comprehensive survey on decentralized modular swarm robotic systems and deployment environments;Abukhalil T;Int J Eng,2013

5. ShreeAS SinghVK SolankiPB BeheraL.Design and development of robotic fish swarm based coast monitoring system. Paper presented at: Proceedings of Conference on Advances in Robotics; July 4–6 2013:1‐6; ACM Pune India New York NY.

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