An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

Abstract

A two-wheeled self-balancing robot (SBR) is a typical example in control systems that works on the principle of an inverted pendulum. In this paper, we experiment to see how the learning and stability performance varies based on Kalman filter introduction for IMU noise filtering and controlling the robot using reinforcement learning. All the implementation is performed in ROS and Gazebo, and Q-learning is implemented using OpenAI (toolkit for development of Reinforcement learning) for ROS, i.e., Openai_ros package. Our work deals with a novel approach of providing the angular output from IMU to Kalman filter and passing it to the input of Q-learning for balancing control. Finally, we analyze the results with and without using Kalman filter from the output of IMU before passing it to Q-learning and evaluate the performance based on robot’s learning behavior and its robustness.