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

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Asita Kumar Rath ◽  
Dayal R. Parhi ◽  
Harish Chandra Das ◽  
Priyadarshi Biplab Kumar ◽  
Manjeet Kumar Mahto
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Turning Angle ◽  
Content Type ◽  
Cluttered Environments ◽  
Hybrid Controller ◽  
Neural Network Controller ◽  
Network Controller

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.

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

2019 ◽  
Vol 7 (3) ◽  
pp. 112-119 ◽  
Author(s):  
Asita Kumar Rath ◽  
Dayal R. Parhi ◽  
Harish Chandra Das ◽  
Priyadarshi Biplab Kumar ◽  
Manoj Kumar Muni ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Path Planning ◽  
Humanoid Robot ◽  
Fuzzy Logic Controller ◽  
Target Location ◽  
Target Position ◽  
Content Type ◽  
Hybrid Controller ◽  
Fuzzy Genetic

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.

Navigational Analysis of Multiple Humanoids Using a Hybridized Rule Base-Sugeno Fuzzy Controller

2020 ◽  
Vol 17 (04) ◽  
pp. 2050017
Author(s):  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Priyadarshi Biplab Kumar ◽  
Asita Kumar Rath
Keyword(s):  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Fuzzy Model ◽  
Humanoid Robots ◽  
Rule Base ◽  
Turning Angle ◽  
Cluttered Environments ◽  
Hybrid Controller ◽  
Final Output ◽  
Set Up

This paper describes a rule base-Sugeno fuzzy hybrid controller for path planning of single as well as multiple humanoid robots in cluttered environments. Initially, sensor outputs regarding the obstacle distances are used as inputs to the rule base model, and turning angle is obtained as the output. The rule-based analysis is used for training the fuzzy controller with membership functions. The output from the rule base model along with other regular inputs is supplied to a Sugeno fuzzy model, and effective turning angle is obtained as the final output to avoid the obstacles present in the environment and navigate the humanoids safely to their target points. The proposed hybrid controller is tested on a V-REP simulation platform, and the simulation results are validated in an experimental set-up. To avoid the possibility of any inter-collision during navigation of multiple humanoids on a common platform, a Petri-net scheme is integrated along with the proposed hybrid model. Finally, the results obtained from simulation and experimental platforms are compared against each other with proper agreement and minimal percentage of deviations. To validate the proposed controller, it has also been tested against another existing navigational approach, and satisfactory performance enhancement has been observed.

Recognition of Paper Currency Research Based on AGA-BP Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 3968-3972
Author(s):  
Xue Xiao ◽  
Qing Hong Wu ◽  
Ying Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Biological Evolution ◽  
Adaptive Genetic Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Paper Currency ◽  
Randomized Search ◽  
Banknote Recognition

The genetic algorithm is a randomized search method for a class of reference biological evolution of the law evolved, with global implicit parallelism inherent and better optimization. This paper presents an adaptive genetic algorithm to optimize the use of BP neural network method, namely the structure of weights and thresholds to optimize BP neural network to achieve the recognition of banknotes oriented. Experimental results show that after using genetic algorithms to optimize BP neural network controller can accurately and quickly achieved recognition effect on banknote recognition accuracy compared to traditional BP neural network has been greatly improved, improved network adaptive capacity and generalization ability.

The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 191-195
Author(s):  
Yong Chao Zhang ◽  
Wen Zhuang Zhao ◽  
Jin Lian Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Intelligent Control System ◽  
Neural Network Controller ◽  
Network Controller ◽  
Controller Performance ◽  
Fuzzy Neural ◽  
Neural Network Structure

How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.

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