Abstract:
Artificial Intelligence has been a topic of great interest in a wide range of fields, such as
gaming, computer vision, and robotics. One of the impressive applications is the AI-based
Alpha Go that has superhuman performance in the games of Go, with the help of Deep
Reinforcement Learning based techniques. Majority of the research in robotics is concerned
with mobile robots, especially mobile robot navigation using Deep Reinforcement Learning.
Our interests focus on investigating whether a Deep Q Learning-based solution is beneficial
to improve the robot behaviors when solving constrained autonomous navigation problems.
This work, focus on implementing a robust, point to point navigation module (local
path planner, decision making and control modules) using Deep Q Learning for autonomous
navigation of indoor robot. And this path planner is able to navigate robot autonomously
through cluttered, unstructured environments satisfying constrains like the robot should not
collide with any static or dynamic obstacles, it should keep a safe distance from all objects
while moving, the robot should traverse on the left-hand side of the corridor and should
navigate through shortest path.
The frameworks used in this work are ROS, OpenAI Gym and Gazebo Simulator. Train ing neural network architecture in Deep Q Learning requires a large data set which is obtained
by stimulating robot on Gazebo environment. And this data set is used by Deep Q Learning
algorithm (which is written as a node in ROS) for training
