This paper presents an intelligent sewing system
for personalized stent graft manufacturing, a challenging
sewing task that is currently performed manually. Inspired by
medical suturing robots, we have adopted a single-sided sewing
technique using a curved needle to perform the task of sewing
stents onto fabric. A motorized surgical needle driver was
attached to a 7 d.o.f robot arm to manipulate the needle with a
second robot controlling the position of the mandrel. A learningfrom-demonstration
approach was used to program the robot
to sew stents onto fabric. The demonstrated sewing skill was
segmented to several phases, each of which was encoded with
a Gaussian Mixture Model. Generalized sewing movements
were then generated from these models and were used for task
execution. During execution, a stereo vision system was adopted
to guide the robots to adjust the learnt movements according
to the needle pose. Two experiments are presented here with
this system and the results show that our system can robustly
perform the sewing task as well as adapt to various needle
poses. The accuracy of the sewing system was within 2mm.