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Bronchoscopic diagnosis and intervention in the lung is a new frontier for steerable needles, where they have the potential to enable minimally invasive, accurate access to small nodules that cannot be reliably accessed today. However, the curved, flexible bronchoscope requires a much longer needle than prior work has considered, with complex interactions between the needle and bronchoscope channel, introducing new challenges in steerable needle control. In particular, friction between the working channel and needle causes torsional windup along the bronchoscope, the effects of which cannot be directly measured at the tip of thin needles embedded with 5 degree-of-freedom magnetic tracking coils. To compensate for these effects, we propose a new torsional deadband-aware Extended Kalman Filter to estimate the full needle tip pose including the axial angle, which defines its steering direction. We use the Kalman Filter estimates with an established sliding mode controller to steer along desired trajectories in lung tissue. We demonstrate that this simple torsional deadband model is sufficient to account for the complex interactions between the needle and endoscope channel for control purposes. We measure mean final targeting error of 1.36 mm in phantom tissue and 1.84 mm in ex-vivo porcine lung, with mean trajectory followingmore »error of 1.28 mm and 1.10 mm, respectively.

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Underactuation is a core challenge associated with controlling soft and continuum robots, which possess theoretically infinite degrees of freedom, but few actuators. However, m actuators may still be used to control a dynamic soft robot in an m-dimensional output task space. In this paper we develop a task-space control approach for planar continuum robots that is robust to modeling error and requires very little sensor information. The controller is based on a highly underactuated discrete rod mechanics model in maximal coordinates and does not require conversion to a classical robot dynamics model form. This promotes straightforward control design, implementation and efficiency. We perform input-output feedback linearization on this model, apply sliding mode control to increase robustness, and formulate an observer to estimate the full state from sparse output measurements. Simulation results show exact task-space reference tracking behavior can be achieved even in the presence of significant modeling error, inaccurate initial conditions, and output-only sensing.