Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano Piazza Leonardo da Vinci, Milano, Italy.
State Key Laboratory of High Performance Complicated, Central South University Changsha, Changsha, China.
Int J Med Robot. 2021 Feb;17(1):1-11. doi: 10.1002/rcs.2193. Epub 2020 Nov 12.
Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot-assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7-degree of freedom (DoF) robot in a surgical operation.
Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network-based manipulability optimization control was implemented on a 7-DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation.
By comparison, the dynamic neural network-based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non-optimized method).
The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation.
手术操作过程的安全性和准确性都至关重要。避免手术机器人安全性问题奇点的有效方法是最大限度地提高机器人辅助手术中的可操作性。本工作的目的是验证一种动态神经网络优化方法，用于优化 7 自由度（DoF）机器人在手术中的可操作性优化控制。
选择圆形、正弦形和螺旋形三种不同的路径来模拟典型的手术任务。基于动态神经网络的可操作性优化控制在 7-DoF 机器人操纵器上实现。在手术过程中，记录机器人操纵器的可操作性和手术的准确性，以验证性能。
通过比较，基于动态神经网络的可操作性优化控制实现了优化的可操作性，但轨迹跟踪的准确性有所下降（全局误差为 1 毫米，而非优化方法的误差为 0.5 毫米）。
这项工作中验证的方法实现了优化的可操作性，但牺牲了误差。未来的工作应该引入以提高手术的准确性。
