Goal-Oriented Navigation with Avoiding Obstacle based on Deep Reinforcement Learning in Continuous Action Space

Obstacle avoidance problems using Deep Reinforcement Learning (DRL) are becoming possible solutions for autonomous mobile robots. In real-world situations with stationary and moving obstacles, mobile robots must be able to navigate to a goal and safely avoid collisions. This work is an extension of...

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Bibliographic Details
Main Authors: Hien, Pham Xuan, Kim, Gon-Woo
Format: Conference Proceeding
Language:English
Subjects:
Aerospace electronics
deep reinforcement learning
Measurement
Navigation
obstacle avoidance
path planning
Q-learning
Reinforcement learning
Robot sensing systems
Sensors
Turning
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Summary:Obstacle avoidance problems using Deep Reinforcement Learning (DRL) are becoming possible solutions for autonomous mobile robots. In real-world situations with stationary and moving obstacles, mobile robots must be able to navigate to a goal and safely avoid collisions. This work is an extension of ongoing research on the navigation approach for a mobile robot. We show that through the proposed DRL, a goal-oriented collision avoidance model can be trained end-to-end without manual turning or supervision by a human operator. We suggest performing the obstacle avoidance algorithm of the mobile robot in both simulated environments and continuous action space of the real world. Finally, we measure and evaluate the obstacle avoidance capability through data collection of hit ratio metrics during robot execution.
ISSN:2642-3901
DOI:10.23919/ICCAS52745.2021.9649898