Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks

Jun Wang

doi:10.1007/978-3-540-88513-9_2

Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks

Jun Wang^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

As the development of automation industry, robot manipulators are needed to work in more and more complex and dynamic environments. An important issue to be considered is how to avoid static or moving obstacles in the workspace. Kinematically redundant manipulators are those having more degrees of freedom than required to perform end-effector moving tasks in a given workspace. Being dexterous and flexible, they have been used for avoiding obstacles or singularity, and optimizing various performance criteria in addition to tracking desired end-effector trajectories. Of those versatile applications, obstacle avoidance is extremely important for successful motion control in the presence of obstacles. © 2008 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	First International Conference, ICIRA 2008, Proceedings
Publisher	Springer Verlag
Pages	10-13
Volume	5314 LNAI
ISBN (Print)	3540885129, 9783540885122
DOIs	https://doi.org/10.1007/978-3-540-88513-9_2
Publication status	Published - 2008
Externally published	Yes
Event	1st International Conference on Intelligent Robotics and Applications, ICIRA 2008 - Wuhan, China Duration: 15 Oct 2008 → 17 Oct 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5314 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Conference on Intelligent Robotics and Applications, ICIRA 2008
Place	China
City	Wuhan
Period	15/10/08 → 17/10/08

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10.1007/978-3-540-88513-9_2

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Wang, J. (2008). Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks. In Intelligent Robotics and Applications: First International Conference, ICIRA 2008, Proceedings (Vol. 5314 LNAI, pp. 10-13). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5314 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-88513-9_2

Wang, Jun. / Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks. Intelligent Robotics and Applications: First International Conference, ICIRA 2008, Proceedings. Vol. 5314 LNAI Springer Verlag, 2008. pp. 10-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks",

abstract = "As the development of automation industry, robot manipulators are needed to work in more and more complex and dynamic environments. An important issue to be considered is how to avoid static or moving obstacles in the workspace. Kinematically redundant manipulators are those having more degrees of freedom than required to perform end-effector moving tasks in a given workspace. Being dexterous and flexible, they have been used for avoiding obstacles or singularity, and optimizing various performance criteria in addition to tracking desired end-effector trajectories. Of those versatile applications, obstacle avoidance is extremely important for successful motion control in the presence of obstacles. {\textcopyright} 2008 Springer Berlin Heidelberg.",

author = "Jun Wang",

year = "2008",

doi = "10.1007/978-3-540-88513-9\_2",

language = "English",

isbn = "3540885129",

volume = "5314 LNAI",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "10--13",

booktitle = "Intelligent Robotics and Applications",

address = "Germany",

note = "1st International Conference on Intelligent Robotics and Applications, ICIRA 2008 ; Conference date: 15-10-2008 Through 17-10-2008",

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Wang, J 2008, Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks. in Intelligent Robotics and Applications: First International Conference, ICIRA 2008, Proceedings. vol. 5314 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5314 LNAI, Springer Verlag, pp. 10-13, 1st International Conference on Intelligent Robotics and Applications, ICIRA 2008, Wuhan, China, 15/10/08. https://doi.org/10.1007/978-3-540-88513-9_2

Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks. / Wang, Jun.
Intelligent Robotics and Applications: First International Conference, ICIRA 2008, Proceedings. Vol. 5314 LNAI Springer Verlag, 2008. p. 10-13 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5314 LNAI).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AB - As the development of automation industry, robot manipulators are needed to work in more and more complex and dynamic environments. An important issue to be considered is how to avoid static or moving obstacles in the workspace. Kinematically redundant manipulators are those having more degrees of freedom than required to perform end-effector moving tasks in a given workspace. Being dexterous and flexible, they have been used for avoiding obstacles or singularity, and optimizing various performance criteria in addition to tracking desired end-effector trajectories. Of those versatile applications, obstacle avoidance is extremely important for successful motion control in the presence of obstacles. © 2008 Springer Berlin Heidelberg.

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U2 - 10.1007/978-3-540-88513-9_2

DO - 10.1007/978-3-540-88513-9_2

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 3540885129

SN - 9783540885122

VL - 5314 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Wang J. Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks. In Intelligent Robotics and Applications: First International Conference, ICIRA 2008, Proceedings. Vol. 5314 LNAI. Springer Verlag. 2008. p. 10-13. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-88513-9_2

Obstacle avoidance for kinematically redundant manipulators based on recurrent neural networks

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