Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909149388865536 |
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| author | Ahmad, Osama Hussain, Zawar Naeem, Hammad |
| author_facet | Ahmad, Osama Hussain, Zawar Naeem, Hammad |
| contents | This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints to a fixed timestamp. In this literature, we have applied a deep deterministic policy gradient (DDPG) algorithm and compared the model's efficiency with dense and sparse rewards. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL Ahmad, Osama Hussain, Zawar Naeem, Hammad Robotics Systems and Control This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints to a fixed timestamp. In this literature, we have applied a deep deterministic policy gradient (DDPG) algorithm and compared the model's efficiency with dense and sparse rewards. |
| title | Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2403.16652 |