Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

Abstract

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.

Object Recognition and Grasping for Collaborative Robots Based on Vision

“…Obtaining visual information from various sensors is a more convenient alternative to obtaining a precise 3D model of an object. The research literature demonstrates excellent results in target classification research [ 8 , 9 , 10 ] and in target detection research [ 11 , 12 ], where the ability of deep learning to automatically learn image features has been extensively investigated. Many researchers have conducted extensive research in the area of robot grasping detection.…”

An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms

“…DRL makes robotic arm control more smart, does not require accurate modeling of the environment, and can compensate for the shortcomings of traditional motion planning methods. Many researchers have recently investigated robotic arm control based on the DRL approach ( Iriondo et al, 2019 ; Moreira et al, 2020 ; Jiang et al, 2021 ; Sekkat et al, 2021 ; Yang Y. et al, 2021 ). Finn et al (2016) proposed an inverse optimal control algorithm based on the DRL approach.…”

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

“…The inverse kinematics of the manipulator play an important role in robotic research.The desired trajectory can be transformed via inverse kinematics into the corresponding joint trajectories [5]. It is also the fundamental technology for solving many problems, such as trajectory tracking [6], object grasping [7], and dynamic analysis [8]. It allows for the joint variables associated with the required task to be determined.…”