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
Hauptverfasser: Cang, Naimeng, Guo, Dongsheng, Qiu, Feng, Chen, Xianjun, Zhang, Weidong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2327-4662, 2327-4662
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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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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3574846