Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network

Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network

Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and c...

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Bibliographic Details
Main Authors: Jian Liu, Xiaona Huang, Yonghong Deng, Canjun Xiao, Zhibin Li
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Machines
Subjects:
robotic positioning accuracy
geometric error
non-geometric error
red billed blue magpie optimizer
particle swarm optimization
graph neural network
Online Access:https://www.mdpi.com/2075-1702/13/6/526
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