期刊论文详细信息
Applied Sciences
Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping
Abdellah Chehri1  Rachid Saadane2  Hiba Sekkat3  Smail Tigani3 
[1] Applied Sciences Department, University of Quebec, Chicoutimi, QC G7H 2B1, Canada;Electrical Engineering Department, Hassania School of Public Labors, Casablanca 8108, Morocco;Engineering Unit, Euromed Research Center, Euro-Mediterranean University, Fes 51, Morocco;
关键词: grasp task;    deep reinforcement learning;    robotic applications;    deep deterministic policy gradient;    autonomous robots;    robotic arm;   
DOI  :  10.3390/app11177917
来源: DOAJ
【 摘 要 】

While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots.

【 授权许可】

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