A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improv...

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Autores principales:	Yong Tao, Fan Ren, He Gao, Tianmiao Wang, Shan Jiang, Yufang Wen, Jiangbo Lan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dynamic target grasping improved geometric particle filtering affine groups robot position servo grasping visual servo control robotics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8185c1d3a5194cf6b698d4664a44131c
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id	oai:doaj.org-article:8185c1d3a5194cf6b698d4664a44131c
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spelling	oai:doaj.org-article:8185c1d3a5194cf6b698d4664a44131c2021-11-11T15:18:31ZA Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter10.3390/app1121102702076-3417https://doaj.org/article/8185c1d3a5194cf6b698d4664a44131c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10270https://doaj.org/toc/2076-3417Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.Yong TaoFan RenHe GaoTianmiao WangShan JiangYufang WenJiangbo LanMDPI AGarticledynamic target graspingimproved geometric particle filteringaffine groupsrobot position servo graspingvisual servo controlroboticsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10270, p 10270 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dynamic target grasping improved geometric particle filtering affine groups robot position servo grasping visual servo control robotics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	dynamic target grasping improved geometric particle filtering affine groups robot position servo grasping visual servo control robotics Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yong Tao Fan Ren He Gao Tianmiao Wang Shan Jiang Yufang Wen Jiangbo Lan A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
description	Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.
format	article
author	Yong Tao Fan Ren He Gao Tianmiao Wang Shan Jiang Yufang Wen Jiangbo Lan
author_facet	Yong Tao Fan Ren He Gao Tianmiao Wang Shan Jiang Yufang Wen Jiangbo Lan
author_sort	Yong Tao
title	A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
title_short	A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
title_full	A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
title_fullStr	A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
title_full_unstemmed	A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter
title_sort	robot dynamic target grasping method based on affine group improved gaussian resampling particle filter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8185c1d3a5194cf6b698d4664a44131c
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A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

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