Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glas...

Full description

Bibliographic Details
Main Authors:	Fusheng Zha, Yizhou Liu, Wei Guo, Pengfei Wang, Mantian Li, Xin Wang, Jingxuan Li
Format:	Article
Language:	English
Published:	MDPI AG 2018-12-01
Series:	Electronics
Subjects:	motion planning constraint manifolds approximate metric projection
Online Access:	https://www.mdpi.com/2079-9292/7/12/395

id	doaj-e3a336170aed4f56a2dd7c3d81896abb
record_format	Article
spelling	doaj-e3a336170aed4f56a2dd7c3d81896abb2020-11-24T23:57:11ZengMDPI AGElectronics2079-92922018-12-0171239510.3390/electronics7120395electronics7120395Learning the Metric of Task Constraint Manifolds for Constrained Motion PlanningFusheng Zha0Yizhou Liu1Wei Guo2Pengfei Wang3Mantian Li4Xin Wang5Jingxuan Li6State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaShenzhen Academy of Aerospace Technology, Shenzhen 518000, ChinaState Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, ChinaFinding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.https://www.mdpi.com/2079-9292/7/12/395motion planningconstraint manifoldsapproximate metricprojection
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Fusheng Zha Yizhou Liu Wei Guo Pengfei Wang Mantian Li Xin Wang Jingxuan Li
spellingShingle	Fusheng Zha Yizhou Liu Wei Guo Pengfei Wang Mantian Li Xin Wang Jingxuan Li Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning Electronics motion planning constraint manifolds approximate metric projection
author_facet	Fusheng Zha Yizhou Liu Wei Guo Pengfei Wang Mantian Li Xin Wang Jingxuan Li
author_sort	Fusheng Zha
title	Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning
title_short	Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning
title_full	Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning
title_fullStr	Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning
title_full_unstemmed	Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning
title_sort	learning the metric of task constraint manifolds for constrained motion planning
publisher	MDPI AG
series	Electronics
issn	2079-9292
publishDate	2018-12-01
description	Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks.
topic	motion planning constraint manifolds approximate metric projection
url	https://www.mdpi.com/2079-9292/7/12/395
work_keys_str_mv	AT fushengzha learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT yizhouliu learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT weiguo learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT pengfeiwang learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT mantianli learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT xinwang learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning AT jingxuanli learningthemetricoftaskconstraintmanifoldsforconstrainedmotionplanning
_version_	1725455214984560640

Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Similar Items