Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm

To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The impro...

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Autores principales:	Qingni Yuan, Junhui Yi, Ruitong Sun, Huan Bai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mechanical arm path planning artificial potential field method rapid expansion random tree algorithm virtual new node Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a063e29667c54b83a16b5ba3ad50386e
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spelling	oai:doaj.org-article:a063e29667c54b83a16b5ba3ad50386e2021-11-25T16:13:10ZPath Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm10.3390/a141103211999-4893https://doaj.org/article/a063e29667c54b83a16b5ba3ad50386e2021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/321https://doaj.org/toc/1999-4893To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic method based on the number of adjacent obstacles to escape from local minima, which solves the local minimum problem of the APF method and improves the search speed. The improved RRT algorithm (I-RRT) changes the selection method of the nearest neighbor node by introducing a triangular nearest neighbor node selection method, adopts an adaptive step and generates a virtual new node strategy to explore the path, and removes redundant path nodes generated by the RRT algorithm, which effectively improves the obstacle avoidance ability and efficiency of the algorithm. Bezier curves are used to fit the final generated path. Finally, an experimental analysis based on Python shows that the search time of the hybrid algorithm in a multi-obstacle environment is reduced to 2.8 s from 37.8 s (classic RRT algorithm), 10.1 s (RRT* algorithm), and 7.4 s (P_RRT* algorithm), and the success rate and efficiency of the search are both significantly improved. Furthermore, the hybrid algorithm is simulated in a robot operating system (ROS) using the UR5 mechanical arm, and the results prove the effectiveness and reliability of the hybrid algorithm.Qingni YuanJunhui YiRuitong SunHuan BaiMDPI AGarticlemechanical armpath planningartificial potential field methodrapid expansion random tree algorithmvirtual new nodeIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 321, p 321 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mechanical arm path planning artificial potential field method rapid expansion random tree algorithm virtual new node Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	mechanical arm path planning artificial potential field method rapid expansion random tree algorithm virtual new node Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Qingni Yuan Junhui Yi Ruitong Sun Huan Bai Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
description	To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic method based on the number of adjacent obstacles to escape from local minima, which solves the local minimum problem of the APF method and improves the search speed. The improved RRT algorithm (I-RRT) changes the selection method of the nearest neighbor node by introducing a triangular nearest neighbor node selection method, adopts an adaptive step and generates a virtual new node strategy to explore the path, and removes redundant path nodes generated by the RRT algorithm, which effectively improves the obstacle avoidance ability and efficiency of the algorithm. Bezier curves are used to fit the final generated path. Finally, an experimental analysis based on Python shows that the search time of the hybrid algorithm in a multi-obstacle environment is reduced to 2.8 s from 37.8 s (classic RRT algorithm), 10.1 s (RRT* algorithm), and 7.4 s (P_RRT* algorithm), and the success rate and efficiency of the search are both significantly improved. Furthermore, the hybrid algorithm is simulated in a robot operating system (ROS) using the UR5 mechanical arm, and the results prove the effectiveness and reliability of the hybrid algorithm.
format	article
author	Qingni Yuan Junhui Yi Ruitong Sun Huan Bai
author_facet	Qingni Yuan Junhui Yi Ruitong Sun Huan Bai
author_sort	Qingni Yuan
title	Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
title_short	Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
title_full	Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
title_fullStr	Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
title_full_unstemmed	Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm
title_sort	path planning of a mechanical arm based on an improved artificial potential field and a rapid expansion random tree hybrid algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a063e29667c54b83a16b5ba3ad50386e
work_keys_str_mv	AT qingniyuan pathplanningofamechanicalarmbasedonanimprovedartificialpotentialfieldandarapidexpansionrandomtreehybridalgorithm AT junhuiyi pathplanningofamechanicalarmbasedonanimprovedartificialpotentialfieldandarapidexpansionrandomtreehybridalgorithm AT ruitongsun pathplanningofamechanicalarmbasedonanimprovedartificialpotentialfieldandarapidexpansionrandomtreehybridalgorithm AT huanbai pathplanningofamechanicalarmbasedonanimprovedartificialpotentialfieldandarapidexpansionrandomtreehybridalgorithm
_version_	1718413272386895872

Path Planning of a Mechanical Arm Based on an Improved Artificial Potential Field and a Rapid Expansion Random Tree Hybrid Algorithm

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