Page 29 - Flexible Robotics in Medicine

Slender snake-like endoscopic robots in surgery 11

from a forward kinematical model or backward kinematical model. Once the workspace of
a snake surgical robot is known, it is essential to plan the motion of the robot to reach the
operational area and manipulate the target. The anatomy of the operating environment is
hard to model, which brings complexity to the robot’s motion planning. Even if the organs
and tissues can be reconstructed in advance, motion planning of the robot should be careful
by considering tissue deformation and collision avoidance. For a snake robot with 20 linked
sections for the exploration of osteolytic lesions, without modeling of the lesion’s cavity,
Liu et al. [69] proposed the motion planning, including collision detection based on sensor
and sampling. Omisore et al. [24] proposed an inverse kinematics (IK) method for the
planning of the path, with collision detection and avoidance at the assistance of virtual
points. Chen et al. [70] considered less sweep area and target reachability as the motion
planning criteria and proposed safety-enhanced planning based on a dynamic neural model.

1.4.3 Control

The snake-like surgical robots own hyperredundancy and unique mechanisms. As a result,
complexities in modeling and motion planning arise, as have been summarized in the above
sections. Moreover, the environment of human anatomy is narrow, curved, and deformable
and thus hard to be modeled, especially when the robot is interacting with it. The robot
itself and the environment in which it operates both enhanced the difficulty in the control
problem.

1.4.3.1 Controlling variables

Position, force, and stiffness are the main issues in controlling the snake-like surgical
robots. Mostly motion control of the snake robots is designed by optimization under
constraints such as interaction with human anatomy, for example, Sen et al. [13] proposed
to control for an 11-DOF snake-like palpation robot based on optimization under constraints
of joint position and velocity limits; Kwok et al. [71] derived the motion modeling of an
articulated snake robot under dynamic active constraints including proximity query status,
haptic information, and visual information, to optimize the configurations and realize
control of human robot interaction; Li et al. [10] proposed optimal control for snake
surgical robot by pursuing the highest stiffness and minimal movement in inverse
kinematical solutions; Smoljkic et al. [32] realized control of a flexible robot for MIS based
on expression graph-based task controller framework by quadratic programming of
constraints of the pose of the tip and shaft. Hybrid motion and force control by Bajo and
Simaan [72] for a multibackbone continuum robot was built in a control framework that
was composed of two separate controllers for the motion and force, respectively,
considering the online estimation of compliance force and motion solution in the
configuration space.