Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL Article Swipe

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Osama Ahmad , Zawar Hussain , Hammad Naeem ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2403.16652

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.

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Artificial Intelligence

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Manipulator (device) Robot manipulator Trajectory Computer science Mobile manipulator Control engineering Control theory (sociology) Robot Artificial intelligence Engineering Mobile robot Physics Control (management) Astronomy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2403.16652
PDF: https://arxiv.org/pdf/2403.16652
OA Status: green
Cited By: 2
Related Works: 10
OpenAlex ID: https://openalex.org/W4393213510

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DOI: https://doi.org/10.48550/arxiv.2403.16652

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Title: Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-03-25

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Authors: Osama Ahmad, Zawar Hussain, Hammad Naeem

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Landing page: https://arxiv.org/abs/2403.16652

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PDF URL: https://arxiv.org/pdf/2403.16652

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Concepts: Manipulator (device), Robot manipulator, Trajectory, Computer science, Mobile manipulator, Control engineering, Control theory (sociology), Robot, Artificial intelligence, Engineering, Mobile robot, Physics, Control (management), Astronomy

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Cited by: 2

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Citations by year (recent): 2024: 2

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Related works (count): 10

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