Behaviour based navigational control of humanoid robot using genetic algorithm technique in cluttered environment

Rath, Asita Kumar; Parhi, Dayal R.; Das, Harish Chandra; Kumar, Priyadarshi Biplab

doi:10.18280/mmc_a.910105

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…They used the sensor information like obstacle distances and location of target as input to the controller and obtained streaming angle as output from the controller. Rath et al 6 presented genetic algorithm-based controller for path planning of a humanoid in complex environments with static and dynamic hurdles. They developed an objective function for path optimization and performed in both simulation and experimental platform.…”

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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“…Lamini et al (2018) have conducted a study for resolving the difficulties encountered in path planning and suggested an improved crossover operator for the method of genetic algorithm. Rath et al (2018b) have implemented the genetic heredity calculation in the path planning of a humanoid robot. The main aim of the investigation was to make a prototype of a navigation controller implementing the genetic algorithm for the course with obstacles.…”

Section: Introductionmentioning

confidence: 99%

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Rath¹,

Parhi

Das

et al. 2020

IJIUS

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PurposeTo navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.Design/methodology/approachThe experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.FindingsFrom the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.Originality/valueWith a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

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“…Navigational analysis of mobile robotic forms using various soft computing methods (Mohanty & Parhi, ; Mohanty & Parhi, ; Pandey & Parhi, ; Parhi, Deepak, Mohana, Ruppa, & Nayak, ; Parhi & Kundu, ) have been attempted by several researchers. Rath et al (Rath, Das, Parhi, & Kumar, ; Rath, Parhi, Das, & Kumar, ; Rath, Parhi, Das, Muni, & Kumar, ) have discussed possible uses of nature inspired algorithms in humanoid navigation.…”

Section: Introductionmentioning

confidence: 99%

Path planning of humanoids based on artificial potential field method in unknown environments

2018

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In this paper, an artificial potential field based navigational controller has been developed for motion planning of humanoid robots. Here, NAO robots are used as the humanoid platform using the underlying principles of potential field based method. The movement of the robot is considered to be under a negative gradient scheme by the combined effect of attractive and repulsive forces generated due to target and obstacles, respectively. The working of the controller is tested in a V‐REP simulation platform, and the simulation results are validated through a real‐time experimental set‐up developed under laboratory conditions. Here, the navigation of both single and multiple humanoids has been attempted. For avoiding intercollision among multiple humanoids during their navigation in a common platform, a Petri‐Net control scheme has been proposed. The results obtained from both the simulation and experimental platforms are compared against each other with a good agreement between them having minimal percentage of deviations. Finally, the proposed controller is also evaluated against another existing navigational model, and a significant performance improvement has been observed.

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Behaviour based navigational control of humanoid robot using genetic algorithm technique in cluttered environment

Cited by 9 publications

References 27 publications

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Path planning of humanoids based on artificial potential field method in unknown environments

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