Depth-based obstacle avoidance through deep reinforcement learning

Obstacle avoidance is an indispensable technique for mobile robots to maneuver safely without collision. In this paper, we propose an end-to-end deep neural network to derive control commands directly from the raw depth images using deep reinforcement learning. The convolutional neural networks are...

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Bibliographic Details
Main Authors: Wu, Keyu, Mahdi Abolfazli Esfahani, Yuan, Shenghai, Wang, Han
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/142309
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Institution: Nanyang Technological University
Language: English