Model-free control for autonomous prevention of adverse events in robotics

Meenakshi Narayan, Ann Majewicz Fey
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Abstract

Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ.Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances.Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.
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机器人自主预防不良事件的无模型控制
引言预防性控制是自主技术中确保系统安全运行的关键功能。安全性最重要的应用之一是机器人辅助针介入。在对组织进行切口时,如果遇到坚硬的薄膜,可能会发生针轴机械弯曲和组织位移等不良事件,从而对器官造成潜在损害:为了防患于未然,我们提出了一个新的控制子程序,该子程序可自主选择:a)当预测到针屈曲或严重组织位移事件时,停止插入程序的反应机制;b)当检测到轻微组织位移时,通过针转向控制继续插入程序的自适应机制。由于未知针-组织动力学的非线性,该子程序采用无模型控制技术开发。首先,通过分析计算动态线性化方程中的快速时变偏假导数,开发了改进版的无模型自适应控制(IMFAC),以提高输出收敛性和对外部干扰的鲁棒性:在 MATLAB 中模拟的非线性系统上比较 IMFAC 和 MFAC 算法,IMFAC 在任意干扰下的输出收敛速度提高了 20%。接下来,IMFAC 与先前工作中的事件预测算法相结合,实时防止针插入过程中的不良事件。在已知环境的明胶组织中插针,成功防止了针弯曲和组织位移事件。在未知环境的生物组织中插针时,使用实时透视成像作为地面实况,以验证能否及时防止不良事件的发生。最后,对所有插入数据进行统计方差分析,显示了预防算法对各种针头和组织环境的稳健性。总体而言,通过自适应和反应式控制防止插针不良事件的成功率为 95%,这对实现机器人插针干预的安全性非常重要。
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