Mobile Robot Navigation Using Deep Reinforcement Learning
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  • Release Date: 2023-03-09
  • autonomous navigation
  • collision avoidance
  • reinforcement learning
Video Introduction

This video is adapted from 10.3390/pr10122748

Learning how to navigate autonomously in an unknown indoor environment without colliding with static and dynamic obstacles is important for mobile robots. The conventional mobile robot navigation system does not have the ability to learn autonomously. Two types of deep Q-learning agents, such as deep Q-network and double deep Q-network agents are proposed to enable the mobile robot to autonomously learn about collision avoidance and navigation capabilities in an unknown environment. For autonomous mobile robot navigation in an unknown environment, the process of detecting the target object is first carried out using a deep neural network model, and then the process of navigation to the target object is followed using the deep Q-network or double deep Q-network algorithm.

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Yusuf, S.H.; Lee, M.R. Mobile Robot Navigation Using Deep Reinforcement Learning. Encyclopedia. Available online: https://encyclopedia.pub/video/video_detail/658 (accessed on 24 April 2024).
Yusuf SH, Lee MR. Mobile Robot Navigation Using Deep Reinforcement Learning. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/658. Accessed April 24, 2024.
Yusuf, Sharfiden Hassen, Min-Fan Ricky Lee. "Mobile Robot Navigation Using Deep Reinforcement Learning" Encyclopedia, https://encyclopedia.pub/video/video_detail/658 (accessed April 24, 2024).
Yusuf, S.H., & Lee, M.R. (2023, March 09). Mobile Robot Navigation Using Deep Reinforcement Learning. In Encyclopedia. https://encyclopedia.pub/video/video_detail/658
Yusuf, Sharfiden Hassen and Min-Fan Ricky Lee. "Mobile Robot Navigation Using Deep Reinforcement Learning." Encyclopedia. Web. 09 March, 2023.