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.

翻译：在机器人最低侵入性手术中测量环境互动力量,可以向外科医生提供方便的反馈,从而解决一个长期的限制。从现有传感器数据中估算出这种力量避免了使用强力传感器改造系统的挑战,但由于机械效应,例如摩擦和机器人机制的合规性等机械效应而困难重重。我们以前已经表明,神经网络可以接受培训,以估计内部机器人联合拳击,从而能够估计外部力量。在这项工作中,我们扩大了估计外部笛轮力和托盘的方法,还提出了一种两步方法,以适应具体的外科手术设置,即对由于仪器轴和坎努拉密封之间以及气轮和耐心体之间的相互作用而导致的力量进行补偿。实验表明,这一方法可以提供平均根位差差差差(RMSE)2N和0.08Nm(RMSE)内外部力量和托盘的估计。此外,两步方法在外科手术设置上仅增加5分钟的时间,大约4分钟收集内部培训数据,1分钟用于培训第二步骤网络。