Path reinforcement learning planning of nuclear power plant UAVs based on dynamic target probability distribution-Reference-Cited by-同舟云学术

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Path reinforcement learning planning of nuclear power plant UAVs based on dynamic target probability distribution

Published:2022-09-01 Issue:9 Volume:52 Page:1642
ISSN:1674-7267
Container-title:SCIENTIA SINICA Informationis
language:en
Short-container-title:Sci. Sin.-Inf.

Author:

周同乐,陈谋

Publisher

Science China Press., Co. Ltd.

Subject

Engineering (miscellaneous),General Computer Science

Link

https://engine.scichina.com/doi/pdf/03C72000E54B402596D4467459F05803

Reference19 articles.

1. Chen D. Nuclear energy and nuclear safety: analysis and reflection about Fukushima nuclear accident. J Nanjing Univ Aeronaut Astronaut, 2012, 44: 597--602.

2. Zhao C K. Development status and 0utlook for nuclear power in China. Nuclear Power Eng, 2018, 39: 1--3.

3. Fei J F, Wang P F, Cheng X P, et al. 2014. A regional simulation study on dispersion of nuclear pollution from the damaged Fukushima Nuclear Power Plant. Sci China Earth Sci, 2014, 44: 271--282.

4. Li G, Chou W. Path planning for mobile robot using self-adaptive learning particle swarm optimization. Sci China Inf Sci, 2018, 61:

5. UAV online path planning algorithm in a low altitude dangerous environment. IEEE/CAA J Autom Sin, 2015, 2: 173-185.

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1. MASAC-based confrontation game method of UAV clusters;SCIENTIA SINICA Informationis;2022-12-01

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