Abstract
Motion planning plays an essential role in motion control for autonomous mobile robots (ARMs). When the information about the operating environment and robot's position obtained from a simultaneous localization and mapping (SLAM) system, a navigation system guarantees that the robot can autonomously and safely move to the desired position in the virtual environments and simultaneously avoid any collisions. This paper presents an intelligent navigation system in unknown 2D environments based on deep reinforcement learning (DRL). Our work was constructed base on the Robot Operating System (ROS). The proposed method's efficiency and accuracy are shown in Gazebo's simulation results and the physical robot's actual results.
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Van, N.T.T., Tien, N.M., Cuong, N.M., Duyen, H.T.K., Duy, N.D. (2021). Constructing an Intelligent Navigation System for Autonomous Mobile Robot Based on Deep Reinforcement Learning. In: Phuong, N.H., Kreinovich, V. (eds) Soft Computing: Biomedical and Related Applications. Studies in Computational Intelligence, vol 981. Springer, Cham. https://doi.org/10.1007/978-3-030-76620-7_22
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