Publikation
Differential Six-Axis Force and Torque Measurement in a Prototype Robotic Surgical Instrument
Daniel Neykov; Timo Markert; Niklas Hellinger; Andreas Theissler; Martin Atzmueller; Sebastian Matich
In: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 11263-11270, IEEE, 10/2025.
Zusammenfassung
In robot-assisted minimally invasive surgery (RMIS), the absence of haptic feedback presents a significant challenge for surgeons in accurately gauging the forces applied during procedures. However, obtaining precise force/torque (F/T) information at the surgical site is challenging. One key obstacle is distinguishing external forces from those induced by the cable-actuated kinematics of the surgical tool. We present a novel method to eliminate this interference by employing differential F/T measurement. We utilize two miniature 6-axis F/T sensors, positioned proximally and distally, to counterbalance the undesired forces and torques generated by the cable-driven system. To demonstrate the efficacy of this approach, we developed an experimental cable-actuated forceps with two degrees of freedom. We conducted a series of dynamic tests, attaching various weight configurations to the gripper to simulate external forces ranging from 0.5 N to 1.5 N. Subsequently, we evaluated three measurement methods: raw distal sensor readings, differential compensation, and a multilayer perceptron (MLP) that processes a sliding window of inputs from both sensors and actuators. Differential compensation improves performance by 70% over the distal sensor alone, achieving a root-mean-square error (RMSE) of 0.15 N and 3 mNm across the entire dataset. The MLP yields a further improvement of 90% lower RMSE relative to the distal sensor, achieving 0.05 N and 0.5 mNm on a test subset of the data not used for training.