Navigation of multiple humanoid robots using hybrid adaptive swarm‐adaptive ant colony optimisation technique

Parhi, Dayal R.; Sahu, Chinmaya; Kumar, Priyadarshi Biplab

doi:10.1002/cav.1802

Cited by 20 publications

(9 citation statements)

References 43 publications

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“…In papers various researchers have used soft computing methods to control various types of robots [121][122]. Swarm intelligence is one of the robust AI techniques being used by researchers to address various optimisation problems [123][124][125][126][127]. Many researchers have used regression based analysis for robot control and navigation [128][129][130].…”

Section: Discussionmentioning

confidence: 99%

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

2020

AMLAI

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The current research focuses on development and analysis of novel Average Neuro-Fuzzy Controller for path planning and navigation of mobile robot in highly cluttered environment. During the investigation various researches related to robot, control and navigation have been analysed. For mapping the environments several distance sensors mounted on the robot are used. The sensors readings about the environments have been segmented into various sectors (front, left, right and back sectors). Using the sensors reading robots negotiate with the obstacles present in the environments during navigation from start to goal point. Experimental and simulation results obtained during the current research from various exercises are in agreement and are within 3%. Comparisons between results show the effectiveness of the proposed technique for robot navigation in complex environments. This technique can be used to address various engineering optimisation problems.

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Section: Discussionmentioning

confidence: 99%

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

2020

AMLAI

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“…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. The inputs to the hybrid controller are front obstacle distance, left obstacle distance and right obstacle distance, and the corresponding output from the controller is turning angle.…”

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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“…Parhi et al designed a hybridized navigational technique by combining adaptive particle swarm optimization and adaptive ant colony optimization. The developed navigational controller takes the obstacle distances as input and provides the required turning angle of the robot as output while heading toward the target position . Patle et al presented a matrix binary code‐based genetic algorithm for mobile robot navigation in both static and dynamic 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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Navigation of multiple humanoid robots using hybrid adaptive swarm‐adaptive ant colony optimisation technique

Cited by 20 publications

References 43 publications

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Implementation of grey wolf optimization controller for multiple humanoid navigation

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