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

A Recurrent Neural Network Approach to Roll Estimation for Needle Steering