Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method

Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These...
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Gespeichert in:

Autor*in:	Reza Mashayekhi [verfasserIn] Mohd Yamani Idna Idris [verfasserIn] Mohammad Hossein Anisi [verfasserIn] Ismail Ahmedy [verfasserIn] Ihsan Ali [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Motion planning path planning RRT RRT-Connect RRT* RRT*-Connect

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 19842-19852
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:19842-19852

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2969316

Katalog-ID:	DOAJ048633402

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520			\|a Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect.
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allfieldsGer	10.1109/ACCESS.2020.2969316 doi (DE-627)DOAJ048633402 (DE-599)DOAJcac4d0838cbb4cf1b1d280e4840adef5 DE-627 ger DE-627 rakwb eng TK1-9971 Reza Mashayekhi verfasserin aut Informed RRT-Connect: An Asymptotically Optimal Single-Query Path Planning Method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect. Motion planning path planning RRT RRT-Connect RRT RRT*-Connect Electrical engineering. Electronics. Nuclear engineering Mohd Yamani Idna Idris verfasserin aut Mohammad Hossein Anisi verfasserin aut Ismail Ahmedy verfasserin aut Ihsan Ali verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 19842-19852 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:19842-19852 https://doi.org/10.1109/ACCESS.2020.2969316 kostenfrei https://doaj.org/article/cac4d0838cbb4cf1b1d280e4840adef5 kostenfrei https://ieeexplore.ieee.org/document/8968352/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 19842-19852
allfieldsSound	10.1109/ACCESS.2020.2969316 doi (DE-627)DOAJ048633402 (DE-599)DOAJcac4d0838cbb4cf1b1d280e4840adef5 DE-627 ger DE-627 rakwb eng TK1-9971 Reza Mashayekhi verfasserin aut Informed RRT-Connect: An Asymptotically Optimal Single-Query Path Planning Method 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect. Motion planning path planning RRT RRT-Connect RRT RRT*-Connect Electrical engineering. Electronics. Nuclear engineering Mohd Yamani Idna Idris verfasserin aut Mohammad Hossein Anisi verfasserin aut Ismail Ahmedy verfasserin aut Ihsan Ali verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 19842-19852 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:19842-19852 https://doi.org/10.1109/ACCESS.2020.2969316 kostenfrei https://doaj.org/article/cac4d0838cbb4cf1b1d280e4840adef5 kostenfrei https://ieeexplore.ieee.org/document/8968352/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 19842-19852
language	English
source	In IEEE Access 8(2020), Seite 19842-19852 volume:8 year:2020 pages:19842-19852
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topic_facet	Motion planning path planning RRT RRT-Connect RRT* RRT*-Connect Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Reza Mashayekhi @@aut@@ Mohd Yamani Idna Idris @@aut@@ Mohammad Hossein Anisi @@aut@@ Ismail Ahmedy @@aut@@ Ihsan Ali @@aut@@
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callnumber-first	T - Technology
author	Reza Mashayekhi
spellingShingle	Reza Mashayekhi misc TK1-9971 misc Motion planning misc path planning misc RRT misc RRT-Connect misc RRT* misc RRT-Connect misc Electrical engineering. Electronics. Nuclear engineering Informed RRT-Connect: An Asymptotically Optimal Single-Query Path Planning Method
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topic_title	TK1-9971 Informed RRT-Connect: An Asymptotically Optimal Single-Query Path Planning Method Motion planning path planning RRT RRT-Connect RRT RRT*-Connect
topic	misc TK1-9971 misc Motion planning misc path planning misc RRT misc RRT-Connect misc RRT* misc RRT*-Connect misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Motion planning misc path planning misc RRT misc RRT-Connect misc RRT* misc RRT*-Connect misc Electrical engineering. Electronics. Nuclear engineering
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title	Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method
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title_full	Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method
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title_sort	informed rrt*-connect: an asymptotically optimal single-query path planning method
callnumber	TK1-9971
title_auth	Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method
abstract	Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect.
abstractGer	Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect.
abstract_unstemmed	Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT-Connect, which is the informed version of RRT-Connect that uses direct sampling after the first solution found. Unlike RRT-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT-Connect in comparison with RRT, Informed RRT, and RRT-Connect.
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title_short	Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method
url	https://doi.org/10.1109/ACCESS.2020.2969316 https://doaj.org/article/cac4d0838cbb4cf1b1d280e4840adef5 https://ieeexplore.ieee.org/document/8968352/ https://doaj.org/toc/2169-3536
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Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method

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