Autonomous Navigation of Wheeled Robot using a Deep Reinforcement Learning Based Approach

In this research we develop a deep reinforcement learning-based method for autonomous robot navigation. Our approach in this study is based on DDPG and one of its improved versions named SD3. We did some modifications on this algorithm to make it proper for autonomous navigation problems and optimize it for this problems. The modified algorithm can work with high dimensional state spaces because of using convolutional layers. Also we propose two reward terms include linear velocity reward and angular velocity penalty to encourage robot to move faster with smoother movements. For generalizing the algorithm we used an algorithm for randomly changing shape, layout and number of obstacles in the environment. And to speed up the learning process and improving the robot operation, we normalized all input data. Finally, the proposed algorithm is implemented with ROS and Gazebo and the results show improvement versus the main SD3 and DDPG algorithms.