Design and development of a Stewart platform assisted and navigated transsphenoidal surgery

Abstract

In this study, technical details of a Stewart platform (SP) based robotic system as an endoscope positioner and holder for endoscopic transsphenoidal surgery are presented. Inverse and forward kinematics, full dynamics, and the Jacobian matrix of the robotic system are derived and simulated in MATLAB/Simulink. The required control structure for the trajectory and position control of the SP is developed and verified by several experiments. The robotic system can be navigated using a six degrees of freedom (DOF) joystick and a haptic device with force feedback. Position and trajectory control of the SP in the joint space is achieved using a new model-free intelligent PI (iPI) controller and it is compared with the classical PID (proportional-integral-derivative) controller. Trajectory tracking experimental results showed that the tracking performance of iPI is better than that of PID and the total RMSE of the trajectory tracking is decreased by 17.64% using the iPI controller. The validity of the robotic system is proven in the endoscopic transsphenoidal surgery performed on a realistic head model in the laboratory and on a cadaver in the Institute of Forensic Medicine. The key feature of the system developed here is to operate the endoscope via the joystick or haptic device with force feedback under iPI control. Usage of this system helps surgeons in long, fatiguing, and complex operations. This system can generate new possibilities for transsphenoidal surgery such as fully automated robotic surgery systems.

An alternative to proportional‐integral and proportional‐integral‐derivative regulators: Intelligent proportional‐derivative regulators

“…The numerous successful concrete applications (see, e.g., References 7‐9, and the corresponding bibliographies for references until the beginning of 2020) demonstrate that those aims have been fulfilled to a great extent. See, for example, the recent appraisal in Reference 10: “MFC is computationally efficient, easily deployable even on small embedded devices, and can be implemented in real time.” Several conclusive concrete comparisons with PIs and PIDs have been published (see, e.g., References 11‐24). Here we suggest a single universal feedback loop, that is, the intelligent Proportional‐Derivative controller, or iPD , which is derived from the ultra‐local model of order 2 in the sense of Reference 7 u and y are the input and output variables. The time‐varying quantity F corresponds to the poorly known plant and to the disturbances; is an estimate of F . The constant is chosen such that the three terms of Equation (2) are of the same magnitude. is the reference trajectory. is the tracking error. The constants and are the gains.…”

An alternative to proportional-integral and proportional-integral-derivative regulators: Intelligent proportional-derivative regulators

“…See, e.g., the recent appraisal in [4]: "MFC is computationally efficient, easily deployable even on small embedded devices, and can be implemented in real time." Several conclusive concrete comparisons with PIs and PIDs have been published (see, e.g., [2], [3], [8], [13], [22], [27], [29], [35], [39], [41], [43], [46], [54], [55]). 1 Here we suggest a single universal feedback loop, i.e., the intelligent Proportional-Derivative controller, or iPD,…”

Trajectory-tracking of 6-RSS Stewart-Gough manipulator by feedback-linearization control using a novel inverse dynamic model based on the force distribution algorithm

“…In addition to its original applications in tire testing and flight simulation, Stewart-Gough mechanism has also found many applications in the fields of industry and aerospace technology, such as CNC machining [4], active vibration isolation [5][6][7], position stabilization and control [8,9], 6-axis force/torque sensing [10,11], 6DOF displacement measurements [12], and applications in medical technologies such as laparoscopy [10,13,14], skull surgery [15] and robot-assisted ultrasound diagnosis [16].…”

“…Figure11shows the positioning error expressed as the Euclidean distance from the desired trajectory using both inverse dynamic models in the no-load case with a quantized reference trajectory.Figures[12][13][14] show the active-joint errors, the positioning error along the base axes, and the orientation errors of the moving platform, respectively, using both inverse dynamic models in the case of putting additional mass of 2 kg on the moving platform.Figure15shows the comparison of the tracking error expressed as the Euclidean distance from the desired trajectory for the five load conditions mentioned above using both inverse dynamic models.Table4shows the root-mean-square (RMS) of the various error terms using both inverse dynamic models in the no-load case and in the case with 2-kg load.As shown in Figures 9-15 and in Table4, tracking an ideal trajectory through feedback-linearization using the proposed inverse dynamic model with the complex trajectory designed for testing purpose is better than that using the Lagrangian inverse dynamic model with the same trajectory in terms of positioning and orientation. As for tracking the quantized trajectory, the performance achieved using the proposed inverse dynamic model is about 2 times than that using the Lagrangian model.…”