Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map

doi:10.1007/s11633-020-1260-1

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map
	Tri Wahyu Utomo, Adha Imam Cahyadi, Igi Ardiyanto
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2021
卷号	18 期号:2 页码:277-287
摘要	Perception and manipulation tasks for robotic manipulators involving highly-cluttered objects have become increasingly indemand for achieving a more efficient problem solving method in modern industrial environments. But, most of the available methods for performing such cluttered tasks failed in terms of performance, mainly due to inability to adapt to the change of the environment and the handled objects. Here, we propose a new, near real-time approach to suction-based grasp point estimation in a highly cluttered environment by employing an affordance-based approach. Compared to the state-of-the-art, our proposed method offers two distinctive contributions. First, we use a modified deep neural network backbone for the input of the semantic segmentation, to classify pixel elements of the input red, green, blue and depth (RGBD) channel image which is then used to produce an affordance map, a pixel-wise probability map representing the probability of a successful grasping action in those particular pixel regions. Later, we incorporate a high speed semantic segmentation to the system, which makes our solution have a lower computational time. This approach does not need to have any prior knowledge or models of the objects since it removes the step of pose estimation and object recognition entirely compared to most of the current approaches and uses an assumption to grasp first then recognize later, which makes it possible to have an object-agnostic property. The system was designed to be used for household objects, but it can be easily extended to any kind of objects provided that the right dataset is used for training the models. Experimental results show the benefit of our approach which achieves a precision of 88.83%, compared to the 83.4% precision of the current state-of-the-art.
关键词	Grasping point estimation household objects red, green, blue and depth (RGBD) channel image semantic segmentation cluttered environment
DOI	10.1007/s11633-020-1260-1
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44022
专题	学术期刊_Machine Intelligence Research
作者单位	Department of Electrical Engineering and Information Technology, Faculty of Engineering, University of Gadjah Mada, Yogyakarta 55281, Indonesia
推荐引用方式 GB/T 7714	Tri Wahyu Utomo, Adha Imam Cahyadi, Igi Ardiyanto. Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map[J]. International Journal of Automation and Computing,2021,18(2):277-287.
APA	Tri Wahyu Utomo, Adha Imam Cahyadi, Igi Ardiyanto.(2021).Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map.International Journal of Automation and Computing,18(2),277-287.
MLA	Tri Wahyu Utomo, Adha Imam Cahyadi, Igi Ardiyanto."Suction-based Grasp Point Estimation in Cluttered Environment for Robotic Manipulator Using Deep Learning-based Affordance Map".International Journal of Automation and Computing 18.2(2021):277-287.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
IJAC-2020-05-113.pdf（913KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载