Yongyin Ye

A Partition-Learning-Selection-Augmentation (PLSA) Framework to Solve Forward Kinematics of Parallel Robots

The persistent multi-solution challenge in parallel robots’ forward kinematics (FK) has impeded high-precision real-time control. Current data-driven approaches face limitations in predicting accurate and unique solutions, ensuring cross-architectural generalizability, and validating results through