Service Boy Robot Path Planner by Deep Q Network
Rawin Chaisittiporn · 2018
Abstract—This paper has studied how to use Deep Q Network (DQN) with ROS for path planner of service boy robot. We use pymunk library of Python for training the neuron network to learn the best action for various input states, by principle of reinforcement learning. Additionally, we use pygame library for learning simulation, observation and evaluation graphically. We have designed input states, output actions, and hidden layers of the neuron network. After training in predefined episodes we use the neuron network to predict the action of TurtleBot3 real robot. We use ROS for robot operation and use ROS slam_gmapping, amcl, except costmap and planners (both global and local planner). Instead, we use well trained Deep Q Network to predict the action of the robot. The result shows that well trained Deep Q Network has more efficient than original ROS planners. It can navigate to any place in the map and can reach the goal by obstacle avoidance. Absolutely, it can move to any destination in the map by orientation ignorance and reduce the distance accuracy between destination and the robot. Absolutely, it can suitably perform a role of service boy, like in the restaurant. Keywords-DQN, Reinforcement Learning; ROS Navigation; Path planner; Service boy robot