scholarly journals Whole-Body Joint Angle Estimation for Real-Time Humanoid Robot Imitation Based on Gaussian Process Dynamical Model and Particle Filter

2019 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Jian Mi ◽  
Yasutake Takahashi
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Humanoid Robot ◽  
Joint Angle ◽  
Computational Cost ◽  
High Dimensional ◽  
Motion Model ◽  
Joint Angles ◽  
Compact State ◽  
Low Computational Cost

Real-time imitation enables a humanoid robot to mirror the behavior of humans, being important for applications of human–robot interaction. For imitation, the corresponding joint angles of the humanoid robot should be estimated. Generally, a humanoid robot comprises dozens of joints that construct a high-dimensional exploration space for estimating the joint angles. Although a particle filter can estimate the robot state and provides a solution for estimating joint angles, the computational cost becomes prohibitive given the high dimension of the exploration space. Furthermore, a particle filter can only estimate the joint angles accurately using a motion model. To realize accurate joint angle estimation at low computational cost, Gaussian process dynamical models (GPDMs) can be adopted. Specifically, a compact state space can be constructed through the GPDM learning of high-dimensional time-series motion data to obtain a suitable motion model. We propose a GPDM-based particle filter using a compact state space from the learned motion models to realize efficient estimation of joint angles for robot imitation. Simulations and real experiments demonstrate that the proposed method efficiently estimates humanoid robot joint angles at low computational cost, enabling real-time imitation.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals Bayesian Localization in Real-Time using Probabilistic Maps and Unscented-Kalman-Filters

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Spatial Clustering ◽  
Computational Cost ◽  
Measurement Data ◽  
High Definition ◽  
Probabilistic Maps ◽  
Reference Map

In this paper, based on the fusion of Lidar and Radar measurement data, high-definition probabilistic maps, and a tailored particle filter, a Real-Time Monte Carlo Localization (RT_MCL) method for autonomous cars is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects' positions and have a much higher spatial resolution. On the other hand, Radars are more accurate in measuring objects velocities and perform well in extreme weather conditions. Therefore, the merits of both sensors are combined using the UKF to provide pole-like static-objects pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) algorithm to represent each pole landmarks in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated off-line and extracted from a 3-D lidar to be used by a carefully designed Particle Filter (PF) for accurate ego-car localization. The particle filter is initialized by the combined GPS+IMU reading and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using Iterative Closest Point (ICP) algorithm. The proposed pipeline is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal and lateral localization.

scholarly journals High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

2016 ◽  
Vol 9 (2) ◽  
pp. 23 ◽  
Author(s):  
Sofyan M. A. Hayajneh ◽  
AbdulRahman Rashad ◽  
Omar A. Saraereh ◽  
Obaida Al hazaimeh
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Real Life ◽  
Morphological Operation ◽  
Detection Process ◽  
Good Potential ◽  
Bit Plane ◽  
Noisy Background ◽  
Low Computational Cost ◽  
Bit Plane Slicing

The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process.  The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

scholarly journals Prewhitening High-Dimensional fMRI Data Sets Without Eigendecomposition

2014 ◽  
Vol 26 (5) ◽  
pp. 907-919 ◽  
Author(s):  
Abd-Krim Seghouane ◽  
Yousef Saad
Keyword(s):  
Covariance Matrix ◽  
Mean Squared Error ◽  
Computational Cost ◽  
Original Data ◽  
Fmri Data ◽  
High Dimensional ◽  
Data Sets ◽  
Data Set ◽  
Low Computational Cost ◽  
Eigenvectors And Eigenvalues

This letter proposes an algorithm for linear whitening that minimizes the mean squared error between the original and whitened data without using the truncated eigendecomposition (ED) of the covariance matrix of the original data. This algorithm uses Lanczos vectors to accurately approximate the major eigenvectors and eigenvalues of the covariance matrix of the original data. The major advantage of the proposed whitening approach is its low computational cost when compared with that of the truncated ED. This gain comes without sacrificing accuracy, as illustrated with an experiment of whitening a high-dimensional fMRI data set.

scholarly journals A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation

VLSI Design ◽  
2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
M. El Hassani ◽  
S. Jehan-Besson ◽  
L. Brun ◽  
M. Revenu ◽  
M. Duranton ◽  
...  
Keyword(s):  
Real Time ◽  
Video Segmentation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Motion Information ◽  
Temporal Consistency ◽  
Merging Process ◽  
Specific Order ◽  
Previous Frame ◽  
Low Computational Cost

We propose a time-consistent video segmentation algorithm designed for real-time implementation. Our algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images with few parameters to be set. Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.

Inverse Solution and Optimal Concept Rethink from Human Operation to Industrial Redundant Manipulator

2014 ◽  
Vol 541-542 ◽  
pp. 1140-1145 ◽  
Author(s):  
Mei Ling Wang ◽  
Min Zhou Luo ◽  
Xin Lin
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Inverse Kinematics ◽  
Joint Angle ◽  
Inverse Solution ◽  
Joint Movement ◽  
Redundant Manipulator ◽  
Joint Angles ◽  
Human Arm ◽  
Dual Arm

More and more dual arm robots with redundant manipulator are introduced in industrial fields. Here we focus on this special structure with 7-DOF redundant manipulator, an exhibit analytical and optimal concept was proposed. The formula derivations of inverse kinematics showed that when the redundant joint angle has been obtained, the remaining six joint angles can be derived analytically, and there are eight sets of inverse solution for one giving redundant joint angle. Reversed thinking the joint movement habits, patterns, and frequency of human arm operations, an optimal concept was presented to gain a real time computational efficiency of a direct inverse solution while also achieving the purpose of application.

High Speed Method for CCT Estimation: Fundamental, Implemention and Application in Real-Time Simulation

2017 ◽  
Vol 18 (4) ◽  
Author(s):  
Mehdi Zareian Jahromi ◽  
Shahram Montaser Kouhsari
Keyword(s):  
Kinetic Energy ◽  
Power Systems ◽  
Real Time ◽  
High Speed ◽  
Transient Stability ◽  
Computational Cost ◽  
Time Simulation ◽  
Clearing Time ◽  
Low Computational Cost ◽  
Real Time Application

AbstractThis paper proposes a hybrid method based on corrected kinetic energy to determine the critical clearing time. The proposed method structure has been implemented utilizing network preserving model to take details of power systems into consideration. To implement proposed method, the initial critical point is estimated using new concept of equal area criterion. Critical corrected kinetic energy is obtained using method which determines the amount of severity of generator contribution in a fault scenario. Due to the latter, the behavior of AVR and governor are taken into account. From initial and corrected kinetic energy of generators and consequently system, high precision critical clearing time is calculated. In order to validate the proposed method, some comprehensive case studies have been conducted on the IEEE9-bus, IEEE39-bus and IEEE68-bus test systems. Some comprehensiveness in considering the details, simplicity in implementation and low computational cost are the outstanding features of the proposed approach. Also, simulation results approve that the proposed approach can be used in real-time application without loss of any detail in transient stability assessment.

scholarly journals A Real-Time Upper-Body Robot Imitation System

2019 ◽  
Vol 2 (1) ◽  
pp. 49 ◽  
Author(s):  
Zhijun Zhang ◽  
Yaru Niu ◽  
Lingdong Kong ◽  
Shuyang Lin ◽  
Hao Wang
Keyword(s):  
Real Time ◽  
Humanoid Robot ◽  
Computational Cost ◽  
Human Head ◽  
Inverse Kinematic ◽  
Recognition Algorithm ◽  
Upper Body ◽  
State Recognition ◽  
Hand Motion ◽  
Arm Motions

An upper-body robot imitation (UBRI) system is proposed and developed to enable the human upper body imitation by a humanoid robot in real time. To achieve the imitation of arm motions, a geometry-based analytical method is presented and applied to extracting the joint angles of the human and mapping to the robot. Comparing to the traditional numerical methods of inverse kinematic computations, the geometrical analysis method generates a lower computational cost and maintains good imitation similarity. To map the human head motions to the head of the humanoid robot, a face tracking algorithm is employed to recognize the human face and track the human head poses in real time. A hand extraction and hand state recognition algorithm is proposed to achieve the hand motion mapping. At last, the completion rate and similarity evaluation experiments are conducted to verify the effectiveness of the proposed UBRI system.

scholarly journals Adaptive single-channel EEG artifact removal with applications to clinical monitoring

2021 ◽  
Author(s):  
Matteo Dora ◽  
David holcman
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Low Cost ◽  
Computational Cost ◽  
Clinical Monitoring ◽  
Artifact Removal ◽  
Anesthesia Monitoring ◽  
Data Driven Approach ◽  
Movement Artifacts ◽  
Low Computational Cost

Objective: Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. Methods: We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a datadriven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. Results: We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. Conclusions: We show that our method can successfully attenuate various types of artifacts in single-channel EEG. Significance: Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

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