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Paper
Title

Autonomous Navigation of Mobile Robots in a Dynamic and Partially Observable Environment Using Deep Reinforcement Learning

Year: 1400
COI: ISME29_290
Language: EnglishView: 105
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Authors

Maryam Valipour - Graduate Student, University of Tehran, Tehran
Masoud Shariat Panahi - Associate Professor, University of Tehran, Tehran;

Abstract:

A new approach to the navigation of mobile robots in a dynamic and partially observable environment is proposed. The environment contains an unknown number of stationary and randomly moving obstacles and is partially observable to the robot’s sensors due to their limited visibility ranges. The robot receives information about the environment, including the instantaneous relative positions of the stationary and moving obstacles within its visibility range as well as the position of its target via its LiDAR scanner. The information is fed to a Double Dueling Deep Q Network (D۳QN) that uses an augmented reward function to learn the optimal policy to take the shortest collision-free path to the target.Results from multiple simulated tests in PyGame environment with various levels of complexity show that the proposed approach outperforms existing classical and AI-based navigation strategies in terms of convergence rate and the level of complexity it can handle.

Keywords:

Paper COI Code

This Paper COI Code is ISME29_290. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1238498/

How to Cite to This Paper:

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Valipour, Maryam and Shariat Panahi, Masoud,1400,Autonomous Navigation of Mobile Robots in a Dynamic and Partially Observable Environment Using Deep Reinforcement Learning,29th Annual International Conference of Iranian Association of Mechanical Engineers and 8th International Conference on Thermal Power Plants Industry,Tehran,https://civilica.com/doc/1238498

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Type of center: دانشگاه دولتی
Paper count: 72,847
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