Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning

Author:

Hu Jueming¹,Yang Xuxi²^ORCID,Wang Weichang³^ORCID,Wei Peng⁴^ORCID,Ying Lei⁵^ORCID,Liu Yongming¹^ORCID

Affiliation:

1. School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA

2. Department of Aerospace Engineering, Iowa State University, Ames, IA, USA

3. Department of Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

4. Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC, USA

5. Engineering and Computer Science Department, University of Michigan, Ann Arbor, Ann Arbor, MI, USA

Funder

National Aeronautics and Space Administration (NASA) University Leadership Initiative Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference58 articles.

2. UAV collision avoidance policy optimization with deep reinforcement learning;wulfe,2017

4. Optimizing collision avoidance in dense airspace using deep reinforcement learning;li;arXiv 1912 10146,2019

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