MOTION PLANNING WITH OBSTACLE AVOIDANCE FOR ROBOT MANIPULATORS VIA DEEP REINFORCEMENT LEARNING

Abstract

The integration of Deep Reinforcement Learning (DRL) in robotic motion planning represents a cutting-edge approach to enhancing the adaptability and efficiency of robotic manipulators in complex environments. In this project we trained a UR5 manipulator for autonomous navigation within a 2D environment. Our methodology hinges on the Stable Baselines 3 library and Proximal Policy Optimization (PPO) algorithms, grounded within the PyBullet and Gym simulation platforms. The culmination of our research affirms the thesis that it is indeed feasible to train a manipulator to proficiently navigate a 2D environment using DRL. The implications of this work not only bolster the potential for practical applications in various domains but also pave the way for further advancements in the field of robotics.