DATA FUSION OF ROBOT WRIST FORCES BASED ON FINGER FORCE SENSORS AND MLF NEURAL NETWORK

2005 ◽  
Vol 02 (02) ◽  
pp. 101-111
Author(s):  
LI-BIAO TONG ◽  
WEN-JUN LU ◽  
XIN HONG ◽  
TAO MEI ◽  
KE-JUN XU
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Data Fusion ◽  
Sensor Fusion ◽  
Robot Control ◽  
Experimental Results ◽  
Force Sensors ◽  
Finger Force ◽  
Fusion Methods ◽  
Robot Wrist

Quantitative analysis of wrist forces for robot grippers is an important issue for robot control and operation safety. An approach is proposed to deduce the wrist forces from distributed force sensors in the robot fingers. A multi-layer forward (MLF) neural network is designed to fuse the data from finger force sensors. The experimental results demonstrate that the maximum deducing error of the wrist forces is decreased to 4.8% from 18.7% comparing with previous sensor fusion methods.

scholarly journals PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Formation Process ◽  
Robot Control ◽  
Experimental Results ◽  
The Road ◽  
Mobile Robot Control ◽  
Different Parts ◽  
Training Sets

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.

scholarly journals Train Localization using Unscented Kalman Filter – Based Sensor Fusion

2018 ◽  
Vol 1 (2) ◽  
pp. 35-41
Author(s):  
I Faruqi ◽  
M. B. Waluya ◽  
Y. Y. Nazaruddin ◽  
T. A. Tamba ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Precision Measurement ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Experimental Results ◽  
Fusion Method ◽  
Localization Problem ◽  
Rail Systems ◽  
Fusion Methods

This paper presents an application of sensor fusion methods based on Unscented Kalman filter (UKF) technique for solving train localization problem in rail systems. The paper first reports the development of a laboratory-scale rail system simulator which is equipped with various onboard and wayside sensors that are used to detect and locate the train vehicle movements in the rail track. Due to the low precision measurement data obtained by each individual sensor, a sensor fusion method based on the UKF technique is implemented to fuse the measurement data from several sensors. Experimental results which demonstrate the effectiveness of the proposed UKF-based sensor fusion method for solving the train localization problem is also reported.

scholarly journals Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wan-Yu Deng ◽  
Dan Liu ◽  
Ying-Ying Dong
Keyword(s):  
Feature Selection ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Missing Values ◽  
High Dimensional Data ◽  
Complete Data ◽  
Experimental Results ◽  
High Dimensional ◽  
Multimodal Data ◽  
Fusion Methods

Due to missing values, incomplete dataset is ubiquitous in multimodal scene. Complete data is a prerequisite of the most existing multimodality data fusion methods. For incomplete multimodal high-dimensional data, we propose a feature selection and classification method. Our method mainly focuses on extracting the most relevant features from the high-dimensional features and then improving the classification accuracy. The experimental results show that our method produces considerably better performance on incomplete multimodal data such as ADNI dataset and Office dataset, compared to the case of complete data.

scholarly journals Data fusion challenges in precision beekeeping: a review

2020 ◽  
Keyword(s):  
Data Fusion ◽  
Sensor Fusion ◽  
Input Data ◽  
Data Series ◽  
Time Data ◽  
Related Data ◽  
Multiple Parameters ◽  
Multiple Data ◽  
Fusion Methods ◽  
Global Research

The objective of precision beekeeping is to minimize resource consumption and maximize productivity of bees. This is achieved by detecting and predicting beehive states by monitoring apiary and beehive related parameters like temperature, weight, humidity, noise, vibrations, air pollution, wind, precipitation, etc. These parameters are collected as a raw input data by use of multiple different sensory devices, and are often imperfect and require creation of correlation between time data series. Currently, most researches focus on monitoring and processing each parameter separately, whereas combination of multiple parameters produces information that is more sophisticated. Raw input data sets that complement one another could be pre-processed by applying data fusion methods to achieve understanding about global research subject. There are multiple data fusion methods and classification models, distinguished by raw input data type or device usage, whereas sensor related data fusion is called sensor fusion. This paper analyses existing data fusion methods and process in order to identify data fusion challenges and correlate them with precision beekeeping objectives. The research was conducted over a period of 5 months, starting from October, 2019 and was based on analysis and synthesis of scientific literature. The conclusion was made that requirement of data fusion appliance in precision beekeeping is determined by a global research objective, whereas input data introduces main challenges of data and sensor fusion, as its attributes correlate with potential result.

Estimation of wrist force/torque using data fusion of finger force sensors

Measurement ◽  
2004 ◽  
Vol 36 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Ke-Jun Xu ◽  
Qiao-Li Li ◽  
Tao Mei ◽  
Ting Wu
Keyword(s):  
Data Fusion ◽  
Force Sensors ◽  
Finger Force ◽  
Using Data

scholarly journals Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments

2021 ◽  
Vol 11 (9) ◽  
pp. 3921
Author(s):  
Paloma Carrasco ◽  
Francisco Cuesta ◽  
Rafael Caballero ◽  
Francisco J. Perez-Grau ◽  
Antidio Viguria
Keyword(s):  
Sensor Fusion ◽  
Computational Efficiency ◽  
Probabilistic Approach ◽  
Laser Scanner ◽  
Industrial Applications ◽  
Experimental Results ◽  
Added Value ◽  
Aerial Robots ◽  
3D Localization ◽  
High Level

The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.

Quantitative Analysis for SF6 and its Compositions in GIS

2012 ◽  
Vol 562-564 ◽  
pp. 1336-1339
Author(s):  
Hai Lun Wang ◽  
Jian Wei Shen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Traditional Method ◽  
Particle Swarm ◽  
Prediction Errors ◽  
Swarm Optimization ◽  
Training Analysis ◽  
Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

Sign in / Sign up

Export Citation Format

Share Document