Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments. The learning agent finds an obstacle-free way around the provided rough, global path by only depending on the observations from a forward-facin...

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Bibliografiske detaljer

Principais autores:	Halil Ibrahim Ugurlu, Xuan Huy Pham, Erdal Kayacan
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2022-10-01
Serier:	Robotics
Fag:	deep reinforcement learning obstacle avoidance quadrotors sim-to-real transfer
Online adgang:	https://www.mdpi.com/2218-6581/11/5/109
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