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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Vydáno v:IEEE internet of things journal Ročník 12; číslo 15; s. 30662 - 30675
Hlavní autoři: Cang, Naimeng, Guo, Dongsheng, Qiu, Feng, Chen, Xianjun, Zhang, Weidong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acceleration
Acceleration-level
gradient-dynamics and neurodynamics
Manipulators
Mobile robots
Motion planning
Neural networks
omnidirectional mobile robotic manipulators (OMRMs)
Optimization
Performance analysis
Performance indices
Planning
quadratic program (QP)
Quadratic programming
Reinforcement learning
repetitive motion planning (RMP)
Robot arms
Robot sensing systems
Vectors
Wheels
ISSN:2327-4662, 2327-4662
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Shrnutí: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