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

Abstract

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 continuous trajectory experiments. To address these issues, this work proposes the Partition-Learning-Selection-Augmentation (PLSA) framework, which systematically resolves FK multi-solution challenges. PLSA clusters potential solutions through data partitioning, predicts all feasible solutions in parallel using deep neural networks (DNNs), integrates a selection mechanism to identify optimal solutions, and refines accuracy via the Newton-Raphson method. Cross-configuration tests on Stewart and 3-RRS parallel robots validate PLSA’s adaptability to different architectures, achieving at least 98.99% accuracy and a computation speed of approximately 30Hz. Additionally, three neural networks (CNN, KAN, and Transformer) are implemented and compared in the Learning-based Selection module, demonstrating PLSA’s generalizability across diverse networks. Comparative studies against analytical, numerical iterative, and prior data-driven methods confirm PLSA’s unique multi-solution resolution capability, delivering submillimeter accuracy with millisecond-level computation, thus establishing a real-time FK calculation methodology.