Deep Reinforcement Learning Based Mobile Robot Navigation in Unknown Indoor Environments
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Date
2021-05-21
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Yalova University
Abstract
The importance of autonomous robots has been increasing day by day with the development of technology. Difficulties in performing many tasks such as target recognition, navigation, and obstacle avoidance autonomously by mobile robots are problems that must be overcome. In recent years, the use of deep reinforcement learning algorithms in robot navigation has been increasing. One of the most important reasons why deep reinforcement learning is preferred over traditional algorithms is that robots can learn the environments by themselves without any prior knowledge or map in environments with obstacles. This study proposes a navigation system based on the dueling deep Q network algorithm, which is one of the deep reinforcement learning algorithms, for a mobile robot in an unknown environment to reach its target by avoiding obstacles. In the study, a 2D laser sensor and an RGBD camera has been used so that the mobile robot can detect and recognize the static and dynamic obstacles in front of itself, and its surroundings. Robot Operating System (ROS) and Gazebo simulator have been used to model the robot and environment. The experiment results show that the mobile robot can reach its targets by avoiding static and dynamic obstacles in unknown environments.
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Keywords
deep reinforcement learning, mobile robot, navigation, dueling deep Q network, ROS, Gazebo