Inverse models and robust parametric-step neuro-control of a Humanoid Robot

MaloźTamayo, Alejandro J.; VeraźBustamante, Pablo; Ramos, Jéssica Jazmín Maldonado; EnríquezźCobo, Andrés

doi:10.1016/j.neucom.2016.09.107

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Specialized inverse neurocontrol [13]. This type of inertia allows you to teach an inverse neurocontroller online, using the deviation error from the set value e r y .…”

Section: іі the Neurocontrollers Classificationmentioning

confidence: 99%

The Neurocontroller for Satellite Rotation

Shakhovska¹,

Montenegro²,

Kryvenchuk³

et al. 2019

IJISA

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In this work an analysis of neurocontrollers is given. The purpose of this paper is the neurocontroler for attitude control: satellite rotations. The classification of neurocontroller architecture is provided. The pros and cons of different neurocontrollers are described. Two configuration of neural networkfeedforward neural networks with mini-batch descent and modified Elman neural network, are investigated in this work to verify its ability to control the attitude of a satellite. The advantages and disadvantage of different predictive model neurorization systems are described. The class diagram for the simulating of satellite rotation for neural network learning is given. The proposed approach provides the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. The accuracy was calculated. The AI module, after trained for different configurations of wheels, will get commands with desired 3D rotation speeds and control the wheels to achieve the desired rotation speeds.

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“…Specialized inverse neurocontrol [13]. This type of inertia allows you to teach an inverse neurocontroller online, using the deviation error from the set value e r y .…”

Section: іі the Neurocontrollers Classificationmentioning

confidence: 99%

The Neurocontroller for Satellite Rotation

Shakhovska¹,

Montenegro²,

Kryvenchuk³

et al. 2019

IJISA

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“…Son et al (2017) have integrated a PID module with a feed forward neural network to control an SCARA robot's joint positions. Tamayo et al (2017) have analyzed and discussed the control methods of an AH1N2 humanoid robot. , and Rath, Parhi, Das, Muni and Kumar (2018) have used AI techniques for optimizing the path of humanoid robot in an environment of disordered obstacles.…”

Section: Introductionmentioning

confidence: 99%

Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

Rath

Parhi

Das

et al. 2019

IJIUS

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Purpose Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

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“…Son et al [16] combined feed forward neural network controller with a PID module to control a SCARA parallel robot joint angle position. Tamayo et al [17] have developed and analysed the AH1N2 humanoid robot. Mohanta et al [18][19] applied genetic algorithm for systematic search of a safe path in the navigation of a mobile robot towards its target location.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for control and navigation of humanoid robot

Rath¹,

Das²,

Parhi³

et al. 2018

J. MECH. ENG. SCI.

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With the development of science and technology, humanoid robots are widely used among several industries. Humanoid robots are seen as a human replacement in a vast sense. It is a test for analysts to imitate the human aptitude in a counterfeit humanoid robot movement framework. With the developing innovation, the humanoid robots are being created for planetary investigation alongside other versatile robots to additionally enhance the mobility in a thickened domain. This paper is focussed on the development of an Artificial Neural Network based navigational controller for path planning examination of humanoid robot strolling. The path planning analysis is carried out on a NAO humanoid robot. Sensory information regarding obstacle distances and location of target are fed as inputs to the controller and required streaming angle is obtained as the output. Navigational analysis has been performed in both simulation and experimental environments with complicated arena conditions. Finally, a comparison between simulation and experimental results has been done, and the result are found to be in good agreement.

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Inverse models and robust parametric-step neuro-control of a Humanoid Robot

Cited by 8 publications

References 34 publications

The Neurocontroller for Satellite Rotation

The Neurocontroller for Satellite Rotation

Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

Application of artificial neural network for control and navigation of humanoid robot

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