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

2021 ◽  
Author(s):  
Hang Su ◽  
Junhao Zhang ◽  
Ziyu She ◽  
Xin Zhang ◽  
Ke Fan ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Robot Manipulator ◽  
Robot Assisted ◽  
External Disturbances ◽  
Fuzzy Approximation ◽  
Control Framework ◽  
Motion Constraint

AbstractRemote 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.

scholarly journals Disturbance-Observer-Based Fuzzy Control for a Robot Manipulator Using an EMG-Driven Neuromusculoskeletal Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longbin Zhang ◽  
Wen Qi ◽  
Yingbai Hu ◽  
Yue Chen
Keyword(s):  
Fuzzy Control ◽  
Disturbance Observer ◽  
Robot Manipulator ◽  
Complex Environments ◽  
External Disturbances ◽  
System A ◽  
Fuzzy Approximation ◽  
Control Framework ◽  
Approximation Errors ◽  
Neuromusculoskeletal Model

Robot manipulators have been extensively used in complex environments to complete diverse tasks. The teleoperation control based on human-like adaptivity in the robot manipulator is a growing and challenging field. This paper developed a disturbance-observer-based fuzzy control framework for a robot manipulator using an electromyography- (EMG-) driven neuromusculoskeletal (NMS) model. The motion intention (desired torque) was estimated by the EMG-driven NMS model with EMG signals and joint angles from the user. The desired torque was transmitted into the desired velocity for the robot manipulator system through an admittance filter. In the robot manipulator system, a fuzzy logic system, utilizing an integral Lyapunov function, was applied for robot manipulator systems subject to model uncertainties and external disturbances. To compensate for the external disturbances, fuzzy approximation errors, and nonlinear dynamics, a disturbance observer was integrated into the controller. The developed control algorithm was validated with a 2-DOFs robot manipulator in simulation. The results indicate the proposed control framework is effective and crucial for the applications in robot manipulator control.

scholarly journals Toward Teaching by Demonstration for Robot-Assisted Minimally Invasive Surgery

2021 ◽  
pp. 1-11
Author(s):  
Hang Su ◽  
Andrea Mariani ◽  
Salih Ertug Ovur ◽  
Arianna Menciassi ◽  
Giancarlo Ferrigno ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Invasive Surgery ◽  
Robot Assisted ◽  
Teaching By Demonstration

Multimodal data fusion framework enhanced robot-assisted minimally invasive surgery

2021 ◽  
pp. 014233122098435
Author(s):  
Wen Qi ◽  
Hang Su ◽  
Ke Fan ◽  
Ziyang Chen ◽  
Jiehao Li ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Data Fusion ◽  
Invasive Surgery ◽  
Robot Control ◽  
Surgical Robot ◽  
Robot Assisted ◽  
Multimodal Data ◽  
Multimodal Data Fusion ◽  
Fusion Framework

The generous application of robot-assisted minimally invasive surgery (RAMIS) promotes human-machine interaction (HMI). Identifying various behaviors of doctors can enhance the RAMIS procedure for the redundant robot. It bridges intelligent robot control and activity recognition strategies in the operating room, including hand gestures and human activities. In this paper, to enhance identification in a dynamic situation, we propose a multimodal data fusion framework to provide multiple information for accuracy enhancement. Firstly, a multi-sensors based hardware structure is designed to capture varied data from various devices, including depth camera and smartphone. Furthermore, in different surgical tasks, the robot control mechanism can shift automatically. The experimental results evaluate the efficiency of developing the multimodal framework for RAMIS by comparing it with a single sensor system. Implementing the KUKA LWR4+ in a surgical robot environment indicates that the surgical robot systems can work with medical staff in the future.

Sign in / Sign up

Export Citation Format

Share Document