  In robot-assisted minimally invasive surgery (RAMIS), optimal placement of
the surgical robot base is crucial for successful surgery. Improper placement
can hinder performance because of manipulator limitations and inaccessible
workspaces. Conventional base placement relies on the experience of trained
medical staff. This study proposes a novel method for determining the optimal
base pose based on the surgeon's working pattern. The proposed method analyzes
recorded end-effector poses using a machine learning-based clustering technique
to identify key positions and orientations preferred by the surgeon. We
introduce two scoring metrics to address the joint limit and singularity
issues: joint margin and manipulability scores. We then train a multi-layer
perceptron regressor to predict the optimal base pose based on these scores.
Evaluation in a simulated environment using the da Vinci Research Kit shows
unique base pose score maps for four volunteers, highlighting the individuality
of the working patterns. Results comparing with 20,000 randomly selected base
poses suggest that the score obtained using the proposed method is 28.2% higher
than that obtained by random base placement. These results emphasize the need
for operator-specific optimization during base placement in RAMIS.
