1 research outputs found
Estimation of Trocar and Tool Interaction Forces on the da Vinci Research Kit with Two-Step Deep Learning
Measurement of environment interaction forces during robotic
minimally-invasive surgery would enable haptic feedback to the surgeon, thereby
solving one long-standing limitation. Estimating this force from existing
sensor data avoids the challenge of retrofitting systems with force sensors,
but is difficult due to mechanical effects such as friction and compliance in
the robot mechanism. We have previously shown that neural networks can be
trained to estimate the internal robot joint torques, thereby enabling
estimation of external forces. In this work, we extend the method to estimate
external Cartesian forces and torques, and also present a two-step approach to
adapt to the specific surgical setup by compensating for forces due to the
interactions between the instrument shaft and cannula seal and between the
trocar and patient body. Experiments show that this approach provides estimates
of external forces and torques within a mean root-mean-square error (RMSE) of 2
N and 0.08 Nm, respectively. Furthermore, the two-step approach can add as
little as 5 minutes to the surgery setup time, with about 4 minutes to collect
intraoperative training data and 1 minute to train the second-step network