Joint-level force sensing for indirect hybrid force/position control of continuum robots with friction

2020 ◽  
pp. 027836492097972
Author(s):  
Rashid Yasin ◽  
Nabil Simaan
Keyword(s):  
User Study ◽  
Position Control ◽  
Force Sensor ◽  
Ex Situ ◽  
Force Sensing ◽  
Support Vector ◽  
Continuum Robots ◽  
Joint Level ◽  
Obstacle Circumvention ◽  
Port Access

Continuum robots offer the dexterity and obstacle circumvention capabilities necessary to enable surgery in deep surgical sites. They also can enable joint-level ex situ force sensing (JEFS), which provides an estimate of end-effector wrenches given joint-level forces. Prior works on JEFS relied on a restrictive embodiment with minimal actuation line friction and captured model and frictional actuation transmission uncertainties using a configuration space formulation. In this work, we overcome these limitations. First, frictional losses are canceled using a feed-forward term based on support vector regression in joint space. Then, regression maps and their interpolation are used to account for actuation hysteresis. The residual joint-force error is then further minimized using a least-squares model parameter update. An indirect hybrid force/position controller using JEFS is presented with evaluation carried out on a realistic pre-clinically deployable insertable robotic effectors platform (IREP) for single-port access surgery. Automated mock force-controlled ablation, exploration, and knot tightening are evaluated. A user study involving the daVinci Research Kit surgeon console and the IREP as a surgical slave was carried out to compare the performance of users with and without force feedback based on JEFS for force-controlled ablation and knot tightening. Results in automated experiments and a user study of telemanipulated experiments suggest that intrinsic force-sensing can achieve levels of force uncertainty and force regulation errors of the order of 0.2 N. Using JEFS and automated task execution, repeatability, and force regulation accuracy is shown to be comparable to using a commercial force sensor for human-in-the-loop feedback.

Parallel Continuum Robots: Modeling, Analysis, and Actuation-Based Force Sensing

2018 ◽  
Vol 34 (1) ◽  
pp. 29-47 ◽  
Author(s):  
Caroline B. Black ◽  
John Till ◽  
D. Caleb Rucker
Keyword(s):  
Force Sensing ◽  
Continuum Robots ◽  
Modeling Analysis

Design and Analysis of Force Sensor for Condition Monitoring System of Ball Cold Heading Forming

2013 ◽  
Vol 364 ◽  
pp. 253-256 ◽  
Author(s):  
Qi Zhang ◽  
Ying Jun Li ◽  
Ru Jian Ma ◽  
Xiu Hua Men
Keyword(s):  
Condition Monitoring ◽  
Dynamic Performance ◽  
Force Sensor ◽  
Dynamic Measurement ◽  
Wide Frequency Range ◽  
Force Sensing ◽  
Frequency Range ◽  
Advantages And Disadvantages ◽  
Condition Monitoring System ◽  
Forming Defects

In order to solve the forming defects in the steel ball cold heading process, a novel force sensor which chooses the PVDF piezoelectric films as force-sensing elements is designed. The advantages and disadvantages of piezoelectric force sensor on measurement of the cold heading force are compared with existing force sensors. By using FEM, sensor’s linearity and the structure size are analyzed. Compared with the traditional sensor, this structure is more reasonable. The presented PVDF piezoelectric force sensor has wide frequency range, good dynamic performance, and can realize dynamic measurement.

Stiffness Control of Parallel Continuum Robots

2018 ◽  
Author(s):  
Vincent Aloi ◽  
Caroline Black ◽  
Caleb Rucker
Keyword(s):  
Position Control ◽  
Target Position ◽  
Human Robot Interaction ◽  
Force Measurements ◽  
Modeling Framework ◽  
Continuum Robots ◽  
Integral Control ◽  
Control Approach ◽  
Cosserat Rods ◽  
Stiffness Control

Parallel continuum robots can provide compact, compliant manipulation of tools in robotic surgery and larger-scale human robot interaction. In this paper we address stiffness control of parallel continuum robots using a general nonlinear kinetostatic modeling framework based on Cosserat rods. We use a model formulation that estimates the applied end-effector force and pose using actuator force measurements. An integral control approach then modifies the commanded target position based on the desired stiffness behavior and the estimated force and position. We then use low-level position control of the actuators to achieve the modified target position. Experimental results show that after calibration of a single model parameter, the proposed approach achieves accurate stiffness control in various directions and poses.

scholarly journals Static Force Measurement Using Piezoelectric Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Kyungrim Kim ◽  
Jinwook Kim ◽  
Xiaoning Jiang ◽  
Taeyang Kim
Keyword(s):  
Surface Charge ◽  
Piezoelectric Effect ◽  
Force Measurement ◽  
Piezoelectric Materials ◽  
Force Sensor ◽  
Piezoelectric Sensors ◽  
Force Sensing ◽  
Static Force ◽  
Sensing Applications ◽  
Direct Piezoelectric Effect

In force measurement applications, a piezoelectric force sensor is one of the most popular sensors due to its advantages of low cost, linear response, and high sensitivity. Piezoelectric sensors effectively convert dynamic forces to electrical signals by the direct piezoelectric effect, but their use has been limited in measuring static forces due to the easily neutralized surface charge. To overcome this shortcoming, several static (either pure static or quasistatic) force sensing techniques using piezoelectric materials have been developed utilizing several unique parameters rather than just the surface charge produced by an applied force. The parameters for static force measurement include the resonance frequency, electrical impedance, decay time constant, and capacitance. In this review, we discuss the detailed mechanism of these piezoelectric-type, static force sensing methods that use more than the direct piezoelectric effect. We also highlight the challenges and potentials of each method for static force sensing applications.

A Modular 2-DOF Serial Robot Manipulator for Education in Robot Control

2016 ◽  
Author(s):  
Stephen Mascaro
Keyword(s):  
Robot Control ◽  
Inverse Dynamics ◽  
Position Control ◽  
Sliding Mode ◽  
Feedforward Control ◽  
Robot Manipulator ◽  
Impedance Control ◽  
Force Sensor ◽  
Control Techniques ◽  
Serial Robot

This paper describes a modular 2-DOF serial robot manipulator and accompanying experiments that have been developed to introduce students to the fundamentals of robot control. The robot is designed to be safe and simple to use, and to have just enough complexity (in terms of nonlinear dynamics) that it can be used to showcase and compare the performance of a variety of textbook robot control techniques including computed torque feedforward control, inverse dynamics control, robust sliding-mode control, and adaptive control. These various motion control schemes can be easily implemented in joint space or operational space using a MATLAB/Simulink real-time interface. By adding a simple 2-DOF force sensor to the end-effector, the robot can also be used to showcase a variety of force control techniques including impedance control, admittance control, and hybrid force/position control. The 2-DOF robots can also be used in pairs to demonstrate control architectures for multi-arm coordination and master/slave teleoperation. This paper will describe the 2-DOF robot and control hardware/software, illustrate the spectrum of robot control methods that can be implemented, and show sample results from these experiments.

scholarly journals Adaptive force/ position control for dual-arm system based on neural network radial basis function without using a force sensor

2020 ◽  
pp. 1-7
Author(s):  
Luu Thi Hue, Duong Minh Duc, Pham Thuc Anh Nguyen Pham
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Position Control ◽  
Learning Algorithm ◽  
Contact Point ◽  
Force Sensor ◽  
Object A ◽  
Contact Points ◽  
Radial Basis ◽  
Dual Arm

The paper has developed an adaptive algorithm using neural network for controlling dual-arm robotic system in stable holding a rectangle object and moving it to track the desired trajectories. Firstly, an overall dynamic of the system including the dual-arm robot and the object is derived based on Euler-Lagrangian principle. Then based on the dynamics, a controller has proposed to achieve the desired trajectories of the holding object. A radial basis neural network has been applied to compensate uncertainties of system parameters. The adaptive learning algorithm has been derived owning to Lyapunov stability principle to guarantee asymptotical convergence of the closed loop system. Besides, force control at contact point is implemented without the measurements of forces and moments at contact points. Finally, simulation work on Matlab has been carried out to confirm the accuracy and the effectiveness of the proposed controller.

scholarly journals A COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION

2019 ◽  
Vol 59 (4) ◽  
pp. 322-351
Author(s):  
Ján Kačur ◽  
Milan Durdán ◽  
Marek Laciak ◽  
Patrik Flegner
Keyword(s):  
Coal Gasification ◽  
Calorific Value ◽  
Multivariate Adaptive Regression Splines ◽  
Ex Situ ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Underground Coal Gasification ◽  
Process Data ◽  
Adaptive Regression ◽  
Data Driven Modeling

Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.

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