The development of algorithms for automation of subtasks during robotic surgery can be accelerated by the availability of realistic simulation environments. In this work, we focus on one aspect of the realism of a surgical simulator, which is the positional accuracy of the robot. In current simulators, robots have perfect or near-perfect accuracy, which is not representative of their physical counterparts. We therefore propose a pair of neural networks, trained by data collected from a physical robot, to estimate both the controller error and the kinematic and non-kinematic error. These error estimates are then injected within the simulator to produce a simulated robot that has the characteristic performance of the physical robot. In this scenario, we believe it is sufficient for the estimated error used in the simulation to have a statistically similar distribution to the actual error of the physical robot. This is less stringent, and therefore more tenable, than the requirement for error compensation of a physical robot, where the estimated error should equal the actual error. Our results demonstrate that error injection reduces the mean position and orientation differences between the simulated and physical robots from 5.0 mm / 3.6 deg to 1.3 mm / 1.7 deg, respectively, which represents reductions by factors of 3.8 and 2.1.

翻译：开发机器人手术子任务自动化算法时，逼真的仿真环境可加速其进程。本研究聚焦于手术仿真器真实性的一个关键方面——机器人定位精度。现有仿真器中，机器人具有完美或接近完美的定位精度，这与其物理实体存在显著差异。为此，我们提出采用由物理机器人采集数据训练的双神经网络架构，分别估计控制器误差、运动学与非运动学误差。将误差估计值注入仿真器后，生成的仿真机器人能复现物理机器人的特征性能。我们认为，在仿真场景中，使用与物理机器人实际误差具有统计相似分布的估计误差即足以满足需求。相较于要求估计误差等于实际误差的物理机器人误差补偿需求，该条件更为宽松且更易实现。实验结果表明，误差注入使仿真机器人与物理机器人的平均位置差和姿态角差分别从5.0毫米/3.6度降至1.3毫米/1.7度，降幅达3.8倍与2.1倍。