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

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dc.contributor.authorSadykov, Zhengisbek
dc.contributor.authorKhussainov, Tamerlan
dc.date.accessioned2024-07-10T11:37:49Z
dc.date.available2024-07-10T11:37:49Z
dc.date.issued2024-05-03
dc.description.abstractThe 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.en_US
dc.identifier.citationSadykov, Zh., Khussainov, T. (2024). Motion Planning with Obstacle Avoidance for Robot Manipulators via Deep Reinforcement Learning. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/8108
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Open accessen_US
dc.titleMOTION PLANNING WITH OBSTACLE AVOIDANCE FOR ROBOT MANIPULATORS VIA DEEP REINFORCEMENT LEARNINGen_US
dc.typeBachelor's thesisen_US
workflow.import.sourcescience

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