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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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2022-10-01
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| Serie: | Robotics |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2218-6581/11/5/109 |
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