Deep Reinforcement Learning for Quadrotor Path Following and Obstacle Avoidance-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning for Quadrotor Path Following and Obstacle Avoidance

Published:2021 Issue: Volume: Page:563-633
ISSN:1860-949X
Container-title:Deep Learning for Unmanned Systems
language:
Short-container-title:

Author:

Rubí Bartomeu,Morcego Bernardo,Pérez Ramon

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-77939-9_17

Reference103 articles.

1. Aguiar AP, Hespanha JP, Kokotovic PV (2008) Performance limitations in reference tracking and path following for nonlinear systems. AUTOMATICA 44(3):598–610. https://doi.org/10.1016/j.automatica.2007.06.030

2. Al-Kaff A, García F, Martín D, de la Escalera A, Armingol JM (2017) Obstacle detection and avoidance system based on monocular camera and size expansion algorithm for UAVs. SENSORS 17. https://doi.org/10.3390/s17051061

3. Alejo D, Cobano JA, Heredia G, Ollero A (2016) A reactive method for collision avoidance in industrial environments. J Intell Robot Syst 84:745–758. https://doi.org/10.1007/s10846-016-0359-7

4. Ambrosino G, Ariola M, Ciniglio U, Corraro F, De Lellis E, Pironti A (2009) Path generation and tracking in 3-D for UAVs. IEEE Trans Control Syst Technol 17(4):980–988. https://doi.org/10.1109/TCST.2009.2014359

5. Amin R, Aijun L, Shamshirband S (2016) A review of quadrotor UAV: control methodologies and performance evaluation. Int J Autom Control 10(2):87–103. https://doi.org/10.1504/IJAAC.2016.076453

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1. A review on quadrotor attitude control strategies;International Journal of Intelligent Robotics and Applications;2024-01-10

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