Fujii Fumitake, Nonomura Tatsuki, Shiinoki Takehiro
Department of Mechanical Engineering, Yamaguchi University, Japan.
Department of Radiation Oncology, Yamaguchi University, Japan.
Biomed Phys Eng Express. 2021 Aug 9;7(5). doi: 10.1088/2057-1976/ac1988.
This technical note discloses our implementation of a six degree-of-freedom (DOF) high-precision robotic phantom on a commercially available industrial robot manipulator. These manipulators are designed to optimize their set point tracking accuracy as it is the most important performance metric for industrial manipulators. Their in-house controllers are tuned to suppress its error less than a few tens of micrometers. However, the use of industrial robot manipulators in six DOF robotic phantom can be a difficult problem since their in-house controller are not optimized for continuous path tracking in general. Although instantaneous tracking error in a continuous path tracking task will not exceed five millimeters during motion with the in-house controller, it seriously matters for a robotic phantom, as the tracking error should remain within one millimeter in three dimensional space for all time during motion. The difficulty of the task is further increased since the reference trajectory of a robotic phantom, which is a six DOF tumor motion of a patient, cannot be as smooth as the ones used in factories. The present study presents a feedforward controller for a feedback-controlled industrial six DOF robotic manipulator to be used as a six DOF robotic phantom to drive the water equivalent phantom (WEP). We first trained a set of six recurrent neural networks (RNNs) to capture the six DOF input/output behavior of the robotic manipulator controlled by its in-house controller, and we proceed to formulate an iterative learning control (ILC) using the trained model to generate an augmented reference trajectory for a specific patient that enables very high tracking accuracy to that trajectory. Experimental evaluation results demonstrate clear improvements in the accuracy of the proposed robotic phantom compared to our previous robotic phantom, which uses the same manipulator but is driven by a different corrected reference trajectory.
本技术说明披露了我们在商用工业机器人操纵器上实现六自由度（DOF）高精度机器人模型的过程。这些操纵器旨在优化其设定点跟踪精度，因为这是工业操纵器最重要的性能指标。其内部控制器经过调整，以将误差抑制在几十微米以内。然而，在六自由度机器人模型中使用工业机器人操纵器可能是一个难题，因为其内部控制器通常未针对连续路径跟踪进行优化。尽管在使用内部控制器运动期间，连续路径跟踪任务中的瞬时跟踪误差不会超过五毫米，但对于机器人模型来说这很重要，因为在运动过程中，跟踪误差在三维空间中应始终保持在一毫米以内。由于机器人模型的参考轨迹是患者的六自由度肿瘤运动，不像工厂中使用的轨迹那么平滑，因此任务的难度进一步增加。本研究提出了一种用于反馈控制的工业六自由度机器人操纵器的前馈控制器，该操纵器用作六自由度机器人模型来驱动水等效模型（WEP）。我们首先训练了一组六个递归神经网络（RNN），以捕捉由其内部控制器控制的机器人操纵器的六自由度输入/输出行为，然后我们使用训练后的模型制定迭代学习控制（ILC），以生成针对特定患者的增强参考轨迹，从而实现对该轨迹的非常高的跟踪精度。实验评估结果表明，与我们之前的机器人模型相比，所提出的机器人模型的精度有了明显提高，之前的模型使用相同的操纵器，但由不同的校正参考轨迹驱动。
