Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

J. Robotics Pub Date : 2022-06-30 DOI:10.1155/2022/2585656
Malak H. Sayour, Sharbel E. Kozhaya, S. Saab
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引用次数: 16

Abstract

Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.
自主机器人操作:实时、深度学习抓取未知物体的方法
基于视觉的机器人技术和深度学习技术的最新进展使智能系统能够在更广泛的需要对象操作的应用中使用。寻找物体抓取和自主操作的鲁棒解决方案成为许多工程师关注的焦点,并且仍然是现代机器人技术中最棘手的问题之一。本文提出了一种全抓取管道,提出了一种实时数据驱动的深度学习方法,用于机器人抓取未知物体。该方法利用眼手相机采集的RGB-D图像数据,利用视觉伺服技术将感兴趣的目标定位在视场中。我们的方法旨在减少传播误差,消除复杂的手部跟踪算法、图像分割或3D重建的需要。该方法能够有效地生成可靠的多视图目标抓取,而不考虑目标的几何复杂性和物理特性。所提出的系统架构能够实现简单有效的路径生成和实时跟踪控制。此外,我们的系统是模块化的,可靠的,和精确的末端执行器路径生成和控制。我们通过实验证明了整个系统在巴雷特全臂机械手上的有效性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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