An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence

Kumar, Priyadarshi Biplab; Mohapatra, Saktiswarup; Parhi, Dayal R.

doi:10.1002/cav.1858

Cited by 14 publications

(5 citation statements)

References 54 publications

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“…In this research, the obstacle distances sensed by the sensor are fed to the regression controller and the output from the regression controller is served to the ant colony controller to obtain the final output. Kumar et al 9,10 presented regression and fuzzy logic controller technique for the navigation of humanoid from one location to other. Kumar et al 11 proposed design and control of a manipulator arm for pick and place operations.…”

Section: Introductionmentioning

confidence: 99%

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

2020

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SUMMARY This paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits.

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Section: Introductionmentioning

confidence: 99%

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

2020

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“…Rao et al presented grey wolf searching technique for navigation of autonomous mobile robots in various environments with less time, energy, and collision‐free characteristic . Kumar et al proposed various intelligent approaches for smooth and hassle‐free navigation of humanoid robots in complex environments …”

Section: Introductionmentioning

confidence: 99%

Implementation of grey wolf optimization controller for multiple humanoid navigation

Muni

Parhi

Kumar

2020

Computer Animation & Virtual

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In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision-free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri-net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids. K E Y W O R D SGWOC, NAO humanoid, navigation, petri-net model INTRODUCTIONLot of research works have been performed for path navigation of mobile robots, biped robots, and industrial robots by using both classical and artificial intelligent techniques. But there is still some improvement needed to avoid the trapping of robots in local minima. Today, the main challenging task for researchers is the path navigation of humanoids having multidegrees of freedom by using computational methods. Though several researchers are working on it, still it is in virtue to obtain the best intelligent method for effective and efficient navigation. For this purpose, grey wolf optimization controller (GWOC) is considered. The humanoid NAO considered for this research work is developed by ALDEBARAN robotics France, which is having 25 degrees of freedom. 1 There are sensors for detecting the obstacles, cameras to take the images of hurdles, motors, actuators, and batteries to perform the predefined tasks. The obstacle distances and shape of the obstacles are detected by inbuilt ultrasonic sensors and camera mount on the head. The programmed GWOC activates when NAO finds any obstacle in its path while heading toward target. Several researchers have worked toward the navigation of different kinds of robots using many intelligent techniques as path navigation of robots plays a Comp Anim Virtual Worlds. 2020;31:e1919. wileyonlinelibrary.com/journal/cav

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“…Dongre and Raikwal 13 used RA as a user web browsing prediction method based on previous training pattern. Kumar et al [14][15][16][17] have discussed regarding the use of various nature inspired algorithms for navigational analysis of humanoid robots. Al et al 18 used genetic algorithm (GA) to control the motion of a hybrid actuator by optimization of the parameters affecting the smooth movement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

Kumar

Parhi

2019

Robotica

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SUMMARYIn the current investigation, a novel navigational controller has been designed and implemented for humanoids in cluttered environments. Here, regression analysis is hybridized with genetic algorithm (GA) for designing the controller. The obstacle distances collected in the form of sensor outputs are initially fed to the regression controller; and based on the previous training pattern data, an intermediate advancing angle (AA) is obtained as the first output. The intermediate AA obtained from the regression controller along with other inputs is again fed to the GA controller, which generates the final AA as the desired final output to avoid the obstacles present in a complex environment and reach the destination successfully. The working of the controller is tested on a V-REP simulation platform. In the current work, navigation of both single as well as multiple humanoids has been attempted. To avoid inter-collision among multiple humanoids during their navigation in a common platform, a Petri-Net model has been proposed. The simulation results are validated through a real-time experimental platform developed under laboratory conditions. The results obtained from both the simulation and experimental platforms are compared against each other and are found to be in good agreement with acceptable percentage of errors. Finally, the proposed controller is also compared with other existing navigational controller and an improvement in performance has been observed.

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An intelligent navigation of humanoid NAO in the light of classical approach and computational intelligence

Cited by 14 publications

References 54 publications

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Implementation of grey wolf optimization controller for multiple humanoid navigation

Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments

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