Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

• URL: http://arxiv.org/abs/2005.13857v1
• Date: Thu, 28 May 2020 09:15:14 GMT
• Title: Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
• Authors: Hartmut Surmann, Christian Jestel, Robin Marchel, Franziska Musberg, Houssem Elhadj and Mahbube Ardani
• Abstract summary: We present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal. The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal. The map of the environment is unknown. The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot. The navigator/controller network is pretrained in a high-speed, parallel, and self-implemented simulation environment to speed up the learning process and then deployed to the real robot. To avoid overfitting, we train relatively small networks, and we add random Gaussian noise to the input laser data. The sensor data fusion with the RGB-D camera allows the robot to navigate in real environments with real 3D obstacle avoidance and without the need to fit the environment to the sensory capabilities of the robot. To further increase the robustness, we train on environments of varying difficulties and run 32 training instances simultaneously. Video: supplementary File / YouTube, Code: GitHub

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