  We present a novel 3D adaptive observer framework for use in the
determination of subsurface organic tissue temperatures in electrosurgery. The
observer structure leverages pointwise 2D surface temperature readings obtained
from a real-time infrared thermographer for both parameter estimation and
temperature field observation. We introduce a novel approach to decoupled
parameter adaptation and estimation, wherein the parameter estimation can run
in real-time, while the observer loop runs on a slower time scale. To achieve
this, we introduce a novel parameter estimation method known as attention-based
noise-robust averaging, in which surface thermography time series are used to
directly estimate the tissue's diffusivity. Our observer contains a real-time
parameter adaptation component based on this diffusivity adaptation law, as
well as a Luenberger-type corrector based on the sensed surface temperature. In
this work, we also present a novel model structure adapted to the setting of
robotic surgery, wherein we model the electrosurgical heat distribution as a
compactly supported magnitude- and velocity-controlled heat source involving a
new nonlinear input mapping. We demonstrate satisfactory performance of the
adaptive observer in simulation, using real-life experimental ex vivo porcine
tissue data.
