Design and Validation of New Acceleration-Level Repetitive Motion Planning Scheme for Omnidirectional Mobile Robotic Manipulators - Cang, Naimeng; Guo, Dongsheng; Qiu, Feng; Chen, Xianjun; Zhang, Weidong (IEEE internet of things journal, 2025)
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.