SMCSPO based reaction force estimation for 7 DOF robot arm

Abstract In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with serial elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A Deep Neural Network model is trained by synthetic data extracted from STL file of selected objects . For experimental set-up, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation and the DNN model. The best DNN model capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence the proposed design of gripper and the algorithm achieved the recognition of selected objects without need for additional force/torque or tactile sensors.

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Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4036546 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 8

Author(s):

Seyed Fakoorian ◽

Vahid Azimi ◽

Mahmoud Moosavi ◽

Hanz Richter ◽

Dan Simon

Keyword(s):

Kalman Filter ◽

Continuous Time ◽

Lyapunov Functions ◽

Unscented Kalman Filter ◽

Estimation Error ◽

Reaction Force ◽

Force Estimation ◽

Joint Angles ◽

Estimation Errors ◽

Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

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Hybrid Position and Force Control Without Force Sensor

Journal of Robotics and Mechatronics ◽

10.20965/jrm.1996.p0226 ◽

1996 ◽

Vol 8 (3) ◽

pp. 226-234

Author(s):

Kiyoshi Ohishi ◽

◽

Masaru Miyazaki ◽

Masahiro Fujita ◽

Keyword(s):

Degrees Of Freedom ◽

Inverse Dynamics ◽

Hybrid Control ◽

Robot Manipulator ◽

Reaction Force ◽

Force Sensor ◽

Force Estimation ◽

Estimation System ◽

Dynamics Calculation ◽

Torque Observer

Generally, hybrid control is realized by sensor signal feedback of position and force. However, some robot manipulators do not have a force sensor due to the environment. Moreover, a precise force sensor is very expensive. In order to overcome these problems, we propose the estimation system of reaction force without using a force sensor. This system consists of the torque observer and the inverse dynamics calculation. Using both this force estimation system and <I>H</I>∞ acceleration controller which is based on <I>H</I>∞ control theory, it takes into account the frequency characteristics of both sensor noise effect and disturbance rejection. The experimental results in this paper illustrate the fine hybrid control of the three tested degrees-of-freedom DD robot manipulator without force sensor.

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