Data-driven motion estimation for cable-driven end-effectors through parallel 1D-convolution and recurrent neural networks with attention

Minimally invasive surgery requires the inclusion of cable-driven manipulators, due to the advantages in terms of lower inertia, compact design, and remote actuation. However, the nonlinear factors underlying in a cable-driven system bring a challenge for the motion control of the end-effectors of a surgical robot. Further, conventional data-driven approaches for cable-driven systems fail to jointly consider local and temporally sequential information in control signals. To this end, we propose a motion-estimation approach for cable-driven end-effectors, using 1D-convolution and recurrent neural networks with attention, taking into account local and sequential information in modelling control series. Then, we perform cross-domain experiments on our Hefei University of Technology’s Cable-Driven End-effector’s Motion (HFUT-CDEM) dataset, with the experimental results showing that the proposed approach achieves better performance, compared with conventional regression approaches for sequential-data modelling.