MOTION PLANNING WITH OBSTACLE AVOIDANCE FOR ROBOT MANIPULATORS VIA DEEP REINFORCEMENT LEARNING
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Date
2024-05-03
Authors
Sadykov, Zhengisbek
Khussainov, Tamerlan
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
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.
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Type of access: Open access
Citation
Sadykov, Zh., Khussainov, T. (2024). Motion Planning with Obstacle Avoidance for Robot Manipulators via Deep Reinforcement Learning. Nazarbayev University School of Engineering and Digital Sciences