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.
@article{arxiv.2403.16652,
title = {Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL},
author = {Osama Ahmad and Zawar Hussain and Hammad Naeem},
journal= {arXiv preprint arXiv:2403.16652},
year = {2024}
}