4SPRR-SPR parallel robot is a novel closed-loop mechanism. The research of kinematics is the basis of real-time and robust robot control. This paper aims to proposing a method to address a surrogate model of forward kinematics for PMs (Parallel Mechanisms). Herein, the forward kinematics model is derived by training the VQTAM (Vector-Quantified Temporal Associative Memory) network, which originates from a SOM (Self-Organized Mapping). During the processes of training, testing and estimating this neural network, the priority K-means tree search algorithm is utilized, thus improving the training efficacy. Furthermore, LLR (Local Linear Regression), LWR (Local Weighted Linear Regression) and LLE (Local Linear Embedding) algorithms are respectively combined with VQTAM to get three improvement algorithms, aiming to further optimize the prediction accuracy of the networks. To speed up solving the least squared equation in the three algorithms, SVD (Singular Value Decomposition) is introduced. Finally, Data from inverse kinematics by geometric method is obtained, which is for constructing and validating the VQTAM neural network. Results show that the prediction effect of LLE algorithm is better than others, which could be a potential surrogate model to estimate the output of forward kinematics.
A Real-Time Capable Forward Kinematics Algorithm for Cable-Driven Parallel Robots Considering Pulley Kinematics
