Steerable needles are a promising technology for delivering targeted therapies in the body in a minimally-invasive fashion, as they can curve around anatomical obstacles and hone in on anatomical targets. In order to accurately steer them, controllers must have full knowledge of the needle tip's orientation. However, current sensors either do not provide full orientation information or interfere with the needle's ability to deliver therapy. Further, torsional dynamics can vary and depend on many parameters making steerable needles difficult to accurately model, limiting the effectiveness of traditional observer methods. To overcome these limitations, we propose a model-free, learned-method that leverages LSTM neural networks to estimate the needle tip's orientation online. We validate our method by integrating it into a sliding-mode controller and steering the needle to targets in gelatin and ex vivo ovine brain tissue. We compare our method's performance against an Extended Kalman Filter, a model-based observer, achieving significantly lower targeting errors.

中文翻译：

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递归神经网络方法在滚针估计中的应用

可转向针头是一种以微创方式在体内提供靶向疗法的有前途的技术，因为它们可以绕过解剖学障碍物弯曲并磨练解剖学目标。为了准确地操纵它们，控制器必须完全了解针尖的方向。但是，当前的传感器不能提供完整的方向信息，或者会干扰针头进行治疗的能力。此外，扭转动力学可以变化并且取决于许多参数，使得可操纵的针难以精确地建模，从而限制了传统观察者方法的有效性。为了克服这些限制，我们提出了一种无模型的学习方法，该方法利用LSTM神经网络在线估计针尖的方向。我们通过将其集成到滑模控制器中并将针转向明胶和离体绵羊脑组织中的靶标来验证我们的方法。我们将我们的方法的性能与基于模型的观察器Extended Kalman Filter进行了比较，从而显着降低了定位误差。