A Framework for DRL Navigation With State Transition Checking and Velocity Increment Scheduling

To train a mobile robot to navigate using end-to-end approach which maps sensors data into actions, we can use deep reinforcement learning (DRL) method by providing training environments with proper reward functions. Although some studies have shown the success of DRL in navigation task for mobile robots, the method needs appropriate hyperparameter settings such as the environment's timestep size and the robot's velocity range to produce a good navigation policy. The previous existing DRL framework has proposed the use of odometry sensor to generate dynamic timestep size in the environment to solve the mismatch problem between the timestep size and the robot's velocity. However, the framework lacks a procedure for checking terminal conditions which may occur during action executions resulting inconsistency in the environment and poor navigation policies. In the case of navigation task, terminal conditions may happen when the robot achieves the navigation goal position or collides with obstacles while performing an action in one timestep. To cope with this problem, we propose a state transition checking method in the DRL environment which is specific for navigation task that leverages odometry and laser sensor to ensure that the environment follows Markov Decision Process with dynamic timestep size. We also introduce a velocity increment scheduling to stabilize the mobile robot during training. Our experiment results show that state transition checking along with the velocity increment scheduling are able to make the robot navigate faster with higher success rate compared to other existing DRL frameworks.