An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment

Kumar, Priyadarshi Biplab; Sethy, Mukesh; Parhi, Dayal R.

doi:10.1007/s11042-018-6703-0

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…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. Parhi et al 12 combined adaptive swarm optimization method and adaptive ant colony optimization technique for better enhancement of humanoid toward path length.…”

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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“…Pierezan et al (2017) have discussed a modified self-adaptive differential evolution approach for humanoid robot with a series of experiments. Kumar et al (2018a, b), Kumar, Rawat and Parhi (2018) and Kumar, Sethy and Parhi (2018) have studied and analyzed integration of smart algorithms in navigational control of humanoid robotic forms. Fakoor et al (2016) have implemented a sensor integration-based navigational scheme for a humanoid robot.…”

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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An intelligent computer vision integrated regression based navigation approach for humanoids in a cluttered environment

Cited by 8 publications

References 42 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

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

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