End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

doi:doi:10.57702/6o0q75m2

End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

The proposed method uses raw depth images obtained from a front-facing camera and directly generates local motion plans in the form of smooth motion primitives that move a quadrotor to a goal by avoiding obstacles.

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Efe Camci, Erdal Kayacan (2024). Dataset: End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning. https://doi.org/10.57702/6o0q75m2

DOI retrieved: December 16, 2024

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.1909.13599
Author	Efe Camci
More Authors	Erdal Kayacan
Homepage	https://arxiv.org/abs/1904.02579