Neural learning of robot inverse kinematics transformations

1994 ◽  
Author(s):  
M. M. Gupta
Keyword(s):  
Inverse Kinematics ◽  
Neural Learning

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

scholarly journals Stability of the Tracking Problem with Task-Priority Inverse Kinematics

2018 ◽  
Vol 51 (22) ◽  
pp. 121-125 ◽  
Author(s):  
Mathias Hauan Arbo ◽  
Jan Tommy Gravdahl
Keyword(s):  
Inverse Kinematics ◽  
Tracking Problem ◽  
Task Priority

scholarly journals A Novel Inverse Kinematics Method for Upper-Limb Exoskeleton under Joint Coordination Constraints

2020 ◽  
Author(s):  
Stefano Dalla Gasperina ◽  
Keya Ghonasgi ◽  
Ana C. de Oliveira ◽  
Marta Gandolla ◽  
Alessandra Pedrocchi ◽  
...  
Keyword(s):  
Upper Limb ◽  
Inverse Kinematics ◽  
Joint Coordination ◽  
Upper Limb Exoskeleton

scholarly journals Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications

2021 ◽  
Vol 11 (6) ◽  
pp. 2558
Author(s):  
Mario Troise ◽  
Matteo Gaidano ◽  
Pierpaolo Palmieri ◽  
Stefano Mauro
Keyword(s):  
Preliminary Analysis ◽  
Inverse Kinematics ◽  
Control Algorithm ◽  
Dynamic Models ◽  
Robotic Systems ◽  
Inflation Pressure ◽  
Space Applications ◽  
Space Industry ◽  
Rigid Body Model ◽  
Soft Manipulator

The rising interest in soft robotics, combined to the increasing applications in the space industry, leads to the development of novel lightweight and deployable robotic systems, that could be easily contained in a relatively small package to be deployed when required. The main challenges for soft robotic systems are the low force exertion and the control complexity. In this manuscript, a soft manipulator concept, having inflatable links, is introduced to face these issues. A prototype of the inflatable link is manufactured and statically characterized using a pseudo-rigid body model on varying inflation pressure. Moreover, the full robot model and algorithms for the load and pose estimation are presented. Finally, a control strategy, using inverse kinematics and an elastostatic approach, is developed. Experimental results provide input data for the control algorithm, and its validity domain is discussed on the basis of a simulation model. This preliminary analysis puts the basis of future advancements in building the robot prototype and developing dynamic models and robust control.

scholarly journals Optimized stable gait planning of biped robot using multi-objective evolutionary JAYA algorithm

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097634
Author(s):  
Huan Tran Thien ◽  
Cao Van Kien ◽  
Ho Pham Huy Anh
Keyword(s):  
Inverse Kinematics ◽  
Biped Robot ◽  
Step Length ◽  
Jaya Algorithm ◽  
Kinetic Chain ◽  
Biped Walking ◽  
Gait Planning ◽  
Multi Objective ◽  
Simulation And Experiment ◽  
Stable Gait

This article proposes a new stable biped walking pattern generator with preset step-length value, optimized by multi-objective JAYA algorithm. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is applied to derive the specified biped hip and feet positions. The two objectives related to the biped walking stability and the biped to follow the preset step-length magnitude have been fully investigated and Pareto optimal front of solutions has been acquired. To demonstrate the effectiveness and superiority of proposed multi-objective JAYA, the results are compared to those of MO-PSO and MO-NSGA-2 optimization approaches. The simulation and experiment results investigated over the real small-scaled biped HUBOT-4 assert that the multi-objective JAYA technique ensures an outperforming effective and stable gait planning and walking for biped with accurate preset step-length value.

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