Incorporating model predictive control with fuzzy approximation for robot manipulation under remote center of motion constraint

Abstract

Remote center of motion (RCM) constraint has attracted many research interests as one of the key challenges for robot-assisted minimally invasive surgery (RAMIS). Although it has been addressed by many studies, few of them treated the motion constraint with an independent workspace solution, which means they rely on the kinematics of the robot manipulator. This makes it difficult to replicate the solutions on other manipulators, which limits their population. In this paper, we propose a novel control framework by incorporating model predictive control (MPC) with the fuzzy approximation to improve the accuracy under the motion constraint. The fuzzy approximation is introduced to manage the kinematic uncertainties existing in the MPC control. Finally, simulations were performed and analyzed to validate the proposed algorithm. By comparison, the results prove that the proposed algorithm achieved success and satisfying performance in the presence of external disturbances.

Robotics in laparoscopic surgery - A review

“…A recent work proposed and simulated the application of Model Predictive Control (MPC) with fuzzy approximation for active RCM constraint (caused by the incision point) on the industrial serial 7-DOF robot KUKA LWR + used as a laparoscopic robot [187]. The control objective is to make the robot follow a desired trajectory while controlling the position and orientation of the surgical tool simultaneously.…”

“…PosiƟon Control [45,65,71,94,187,189,191,192] Force/Torque Control [190,191] [191] • DisƟnguishes the procedure in progress and determines the next task to be performed (the goal posiƟon) corresponding to each procedure [71,94,189] [ 187,191,192] Figure 18. Classification of control objectives and control inputs of the discussed control systems.…”

“…• MPC + Fuzzy ApproximaƟon to compensate for disturbances [187] • MPC + Supervisory Control and AcƟon SegmentaƟon using mulƟmodal learning [189] Figure 19. Classification of types of controllers used in the discussed control systems.…”

“…Figure 9. Control framework of Model Predictive Control with fuzzy approximation for active RCM constraint[187].…”

Design and Control of a Tendon-Driven Robotic Finger Based on Grasping Task Analysis

“…These hands are typically equipped with multiple joints and fingers, providing a high degree of freedom (DoF) [ 1 , 2 ]. This extensive DoF allows for a wide range of versatile operations, including grasping, rotating, and manipulating various objects encountered in daily life [ 3 ].…”