Learning Control of Robotic Arm Using Deep Q-Neural Network

Enabling robotic systems for autonomous actions such as driverless systems, is a very complex task in real-world scenarios due to uncertainties. Machine learning capabilities have been quickly making their way into autonomous systems and industrial robotics technology. They found many applications in every sector, including autonomous vehicles, humanoid robots, drones and many more. In this research we will be implementing artificial intelligence in robotic arm to be able to solve a complex balancing control problem from scratch, without any feedback loop and using state of the art deep reinforcement learning algorithm named DQN. The benchmark problem that is considered as case study, is balancing an inverted pendulum upward using a six-degrees freedom robot arm. Very simple form of this problem has been solved recently using machine learning however under this thesis we made a very complex system of inverted pendulum and implemented in Robot Operating System (ROS) which is very realistic simulation environment. We have not only succeeded to control the pendulum but also added turbulences on the learned model to study its robustness. We observed how the initial learned model is unstable at the presence of turbulence and how random turbulences helps the system to transform to a more robust model. We have also used the robust model in different environment and showed how the model adopt itself with the new physical properties. Using orientation sensor on the tip of the inverted pendulum to get angular velocity, simulation in ROS and having inverted pendulum on ball joint are few highlighted novelties in this thesis in compare previous publications