Design and Validation of New Acceleration-Level Repetitive Motion Planning Scheme for Omnidirectional Mobile Robotic Manipulators
Achieving repetitive motion planning (RMP) is essential in the study of mobile robot manipulators. This article presents an acceleration-level RMP (ALRMP) scheme for omnidirectional mobile robotic manipulators (OMRMs). Specifically, a new acceleration-level performance index is designed to realize R...
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| Veröffentlicht in: | IEEE internet of things journal Jg. 12; H. 15; S. 30662 - 30675 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Piscataway
IEEE
01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 2327-4662, 2327-4662 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Achieving repetitive motion planning (RMP) is essential in the study of mobile robot manipulators. This article presents an acceleration-level RMP (ALRMP) scheme for omnidirectional mobile robotic manipulators (OMRMs). Specifically, a new acceleration-level performance index is designed to realize RMP using the gradient-dynamics and neurodynamics methods. Leveraging this index and incorporating physical constraints (i.e., position-level, velocity-level, and acceleration-level limits), a novel ALRMP scheme is proposed and analyzed. The scheme is formulated as a quadratic program (QP) and solved using a neural network solver. Comparative simulations conducted on an OMRM demonstrate the effectiveness and superiority of the proposed ALRMP scheme over the velocity-level RMP (VLRMP) scheme. The applicable potential of the proposed ALRMP scheme is further indicated via the real-world experiment on a practical OMRM system. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2327-4662 2327-4662 |
| DOI: | 10.1109/JIOT.2025.3574846 |