Contact force estimation and regulation in active pantographs: An algebraic observability approach

2007 ◽  
Author(s):  
A. Pisano ◽  
E. Usai
Keyword(s):  
Contact Force ◽  
Force Estimation

Leg-orthosis contact force estimation from gait analysis

2020 ◽  
Vol 148 ◽  
pp. 103800
Author(s):  
F. Mouzo ◽  
F. Michaud ◽  
U. Lugris ◽  
J. Cuadrado
Keyword(s):  
Gait Analysis ◽  
Contact Force ◽  
Force Estimation

scholarly journals Contact force estimation in the wheel/rail interface for curving scenarios through regions of reduced adhesion

Mechatronics ◽  
2018 ◽  
Vol 50 ◽  
pp. 321-327 ◽  
Author(s):  
P.D. Hubbard ◽  
N. Farhat ◽  
C.P. Ward ◽  
G.A. Amarantidis
Keyword(s):  
Contact Force ◽  
Force Estimation

Robust contact force estimation for robot manipulators in three-dimensional space

2006 ◽  
Vol 220 (9) ◽  
pp. 1317-1327 ◽  
Author(s):  
J Jung ◽  
J Lee ◽  
K Huh
Keyword(s):  
Contact Force ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Contact Forces ◽  
Robust Estimator ◽  
Force Sensors ◽  
Force Estimation ◽  
Estimation Algorithms

Information on contact forces in robot manipulators is indispensable for fast and accurate force control. Instead of expensive force sensors, estimation algorithms for contact forces have been widely developed. However, it is not easy to obtain the accurate values due to uncertainties. In this article, a new robust estimator is proposed to estimate three-dimensional contact forces acting on a three-link robot manipulator. The estimator is based on the extended Kalman filter (EKF) structure combined with a Lyapunov-based adaptation law for estimating the contact force. In contrast to the conventional EKF the new estimator is designed such that it is robust to the deterministic uncertainties such as the modelling error and the sensing bias. The performance of the proposed estimator is evaluated through simulations of a robot manipulator and demonstrates robustness in estimating the contact force. The estimation results show that it can be potentially used to replace the expensive force sensors in robot applications.

scholarly journals Contact Force Estimation for Serial Manipulator Based on Weighted Moving Average with Variable Span and Standard Kalman Filter with Automatic Tuning

2021 ◽  
Author(s):  
Feng Cao ◽  
Paul D. Docherty ◽  
XiaoQi Chen
Keyword(s):  
Kalman Filter ◽  
Response Time ◽  
Contact Force ◽  
Moving Average ◽  
Automatic Tuning ◽  
Estimation Methods ◽  
Force Estimation ◽  
Motor Current ◽  
Variable Span ◽  
Weighted Moving Average

Abstract Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available. Consequently, the cost of collision recognition could be reduced dramatically.

scholarly journals A learning-based tip contact force estimation method for tendon-driven continuum manipulator

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fan Feng ◽  
Wuzhou Hong ◽  
Le Xie
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Recurrent Neural Network ◽  
Estimation Method ◽  
Training Data ◽  
Force Sensing ◽  
Force Estimation ◽  
Advantages And Disadvantages ◽  
Continuum Manipulator ◽  
Three Degree Of Freedom

AbstractAlthough tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

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