Gestures Identification for Myoelectric Based Control of the Robotic Arm

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Adhau P ◽  
◽  
Kadwane S. G ◽  
Shital Telrandhe ◽  
Rajguru V. S ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Human Hand ◽  
Robot Interaction ◽  
The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

Control of Autonomous Robots Using the Principles of Neuromodulation

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Autonomous Robots ◽  
Neuronal Model ◽  
Autonomous Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Control Approach ◽  
The Neural Network ◽  
Simple Neural Network ◽  
Interesting Behavior

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

Enhanced Transparency for Physical Human-Robot Interaction Using Human Hand Impedance Compensation

2018 ◽  
Vol 23 (6) ◽  
pp. 2662-2670
Author(s):  
Kyeong Ha Lee ◽  
Seung Guk Baek ◽  
Hyuk Jin Lee ◽  
Hyouk Ryeol Choi ◽  
Hyungpil Moon ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Human Hand ◽  
Robot Interaction

scholarly journals Cable Tension Analysis Oriented the Enhanced Stiffness of a 3-DOF Joint Module of a Modular Cable-Driven Human-Like Robotic Arm

2020 ◽  
Vol 10 (24) ◽  
pp. 8871
Author(s):  
Kaisheng Yang ◽  
Guilin Yang ◽  
Chi Zhang ◽  
Chinyin Chen ◽  
Tianjiang Zheng ◽  
...  
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Robot Interaction ◽  
Cable Tension ◽  
Tension Distribution ◽  
Stiffness Model ◽  
Redundantly Actuated ◽  
Objective Function Values

Inspired by the structure of human arms, a modular cable-driven human-like robotic arm (CHRA) is developed for safe human–robot interaction. Due to the unilateral driving properties of the cables, the CHRA is redundantly actuated and its stiffness can be adjusted by regulating the cable tensions. Since the trajectory of the 3-DOF joint module (3DJM) of the CHRA is a curve on Lie group SO(3), an enhanced stiffness model of the 3DJM is established by the covariant derivative of the load to the displacement on SO(3). In this paper, we focus on analyzing the how cable tension distribution problem oriented the enhanced stiffness of the 3DJM of the CHRA for stiffness adjustment. Due to the complexity of the enhanced stiffness model, it is difficult to solve the cable tensions from the desired stiffness analytically. The problem of stiffness-oriented cable tension distribution (SCTD) is formulated as a nonlinear optimization model. The optimization model is simplified using the symmetry of the enhanced stiffness model, the rank of the Jacobian matrix and the equilibrium equation of the 3DJM. Since the objective function is too complicated to compute the gradient, a method based on the genetic algorithm is proposed for solving this optimization problem, which only utilizes the objective function values. A comprehensive simulation is carried out to validate the effectiveness of the proposed method.

scholarly journals Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiubo Zhong ◽  
Caiming Zheng ◽  
Haoxiang Zhang
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Sequence Data ◽  
Spatial Relationship ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Convolutional Network ◽  
Action Sequence ◽  
Interaction System ◽  
High Discrimination

A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

scholarly journals HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK

2018 ◽  
Vol 11 (1) ◽  
pp. 75-84
Author(s):  
Alvin Rindra Fazrie
Keyword(s):  
Neural Network ◽  
Language Acquisition ◽  
Language Processing ◽  
Language Game ◽  
Human Robot Interaction ◽  
Communication Process ◽  
Robot Interaction ◽  
Echo State Networks ◽  
Stochastic Learning ◽  
Communicative Efficiency

ABSTRACT The paper gives an overview about the process between two language processing methods towards Human-robot interaction. In this paper, Echo State Networks and Stochastic-learning grammar are explored in order to get an idea about generating human’s natural language and the possibilities of integrating these methods to make the communication process between robot to robot or robot to human to be more natural in dialogic syntactic language game. The methods integration could give several benefits such as improving the communicative efficiency and producing the more natural communication sentence.   ABSTRAK Tulisan ini memberikan penjabaran mengenai dua metode pemrosesan bahasa alami pada interaksi Manusia dan Robot. Echo State Networks adalah salah satu arsitektur dari Jaringan Syaraf Tiruan yang berdasarkan prinsip Supervised Learning untuk Recurrent Neural Network, dieksplorasi bersama Stochastic-learning Grammar yaitu salah satu framework tata bahasa dengan konsep probabilistik yang bertujuan untuk mendapatkan ide bagaimana proses bahasa alami dari manusia dan kemungkinannya mengintegrasikan dua metode tersebut untuk membuat proses komunikasi antara robot dengan robot atau robot dengan manusia menjadi lebih natural dalam dialogic syntactic language game. Metode integrasi dapat memberikan beberapa keuntungan seperti meningkatkan komunikasi yang efisien dan dapat membuat konstruksi kalimat saat komunikasi menjadi lebih natural. How To Cite : Fazrie, A.R. (2018). HUMAN-ROBOT INTERACTION: LANGUAGE ACQUISITION WITH NEURAL NETWORK. Jurnal Teknik Informatika, 11(1), 75-84.  doi 10.15408/jti.v11i1.6093 Permalink/DOI: http://dx.doi.org/10.15408/jti.v11i1.6093 

Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network

2019 ◽  
Vol 24 (9) ◽  
pp. 6687-6719 ◽  
Author(s):  
Abdel-Nasser Sharkawy ◽  
Panagiotis N. Koustoumpardis ◽  
Nikos Aspragathos
Keyword(s):  
Neural Network ◽  
Human Robot Interaction ◽  
Input Output ◽  
Robot Interaction
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