TY - JOUR
T1 - Trajectory tracking of the robot end effector for the minimally invasive surgeries
AU - De Jesus Rubio, Jose
AU - Cruz, Panuncio
AU - Garcia, Enrique
AU - Juarez, Cesar Felipe
AU - Cruz, David Ricardo
AU - Lopez, Jesus
N1 - Publisher Copyright:
© 2020 Inderscience Enterprises Ltd.
PY - 2020
Y1 - 2020
N2 - The surgery technology has been highly investigated, with the purpose to reach an efficient way of working in medicine. Consequently, robots with small tools have been incorporated in many kind of surgeries to reach the following improvements: the patient gets a faster recovery, the surgery is not invasive, and the robot can access to the body occult parts. In this article, an adaptive strategy for the trajectory tracking of the robot end effector is addressed; it consists of a proportional derivative technique plus an adaptive compensation. The proportional derivative technique is employed to reach the trajectory tracking. The adaptive compensation is employed to reach approximation of some unknown dynamics. The robot described in this study is employed in minimally invasive surgeries.
AB - The surgery technology has been highly investigated, with the purpose to reach an efficient way of working in medicine. Consequently, robots with small tools have been incorporated in many kind of surgeries to reach the following improvements: the patient gets a faster recovery, the surgery is not invasive, and the robot can access to the body occult parts. In this article, an adaptive strategy for the trajectory tracking of the robot end effector is addressed; it consists of a proportional derivative technique plus an adaptive compensation. The proportional derivative technique is employed to reach the trajectory tracking. The adaptive compensation is employed to reach approximation of some unknown dynamics. The robot described in this study is employed in minimally invasive surgeries.
KW - Minimal invasive surgery
KW - Robot
KW - Trajectory tracking
UR - http://www.scopus.com/inward/record.url?scp=85076207819&partnerID=8YFLogxK
U2 - 10.1504/IJBIDM.2020.103843
DO - 10.1504/IJBIDM.2020.103843
M3 - Artículo
SN - 1743-8187
VL - 16
SP - 66
EP - 88
JO - International Journal of Business Intelligence and Data Mining
JF - International Journal of Business Intelligence and Data Mining
IS - 1
ER -