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Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints
IEEE Access, Volume: 8, Pages: 54225 - 54236
Swansea University Author:
Shuai Li
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DOI (Published version): 10.1109/access.2020.2981688
Abstract
Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In...
Published in: | IEEE Access |
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ISSN: | 2169-3536 |
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Institute of Electrical and Electronics Engineers (IEEE)
2020
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URI: | https://cronfa.swan.ac.uk/Record/cronfa53940 |
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2020-10-22T13:45:31.8401236 v2 53940 2020-04-14 Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-04-14 ACEM Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy. Journal Article IEEE Access 8 54225 54236 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 Motion planning, dual robotic manipulators, dynamic neural networks, zeroing neural networks, redundant resolution 18 3 2020 2020-03-18 10.1109/access.2020.2981688 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2020-10-22T13:45:31.8401236 2020-04-14T08:43:58.3922368 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Jinglun Liang 1 Zhihao Xu 2 Xuefeng Zhou 3 Shuai Li 0000-0001-8316-5289 4 Guoliang Ye 5 53940__17055__9e6d028b37ee4a2c8cddfe410af37484.pdf 53940.pdf 2020-04-14T08:45:59.8866167 Output 2573292 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/ |
title |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
spellingShingle |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints Shuai Li |
title_short |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
title_full |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
title_fullStr |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
title_full_unstemmed |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
title_sort |
Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints |
author_id_str_mv |
42ff9eed09bcd109fbbe484a0f99a8a8 |
author_id_fullname_str_mv |
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li |
author |
Shuai Li |
author2 |
Jinglun Liang Zhihao Xu Xuefeng Zhou Shuai Li Guoliang Ye |
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Journal article |
container_title |
IEEE Access |
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8 |
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54225 |
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2020 |
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Swansea University |
issn |
2169-3536 |
doi_str_mv |
10.1109/access.2020.2981688 |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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description |
Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy. |
published_date |
2020-03-18T07:40:28Z |
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1829812126156324864 |
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11.058331 |