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.

Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhishek Kumar Kashyap ◽  
Dayal R. Parhi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Search Algorithm ◽  
Optimal Path ◽  
Humanoid Robots ◽  
Steering Angle ◽  
Content Type ◽  
Hybrid Controller ◽  
Humanoid Nao

Purpose This paper aims to outline and implement a novel hybrid controller in humanoid robots to map an optimal path. The hybrid controller is designed using the Owl search algorithm (OSA) and Fuzzy logic. Design/methodology/approach The optimum steering angle (OS) is used to deal with the obstacle located in the workspace, which is the output of the hybrid OSA Fuzzy controller. It is obtained by feeding OSA's output, i.e. intermediate steering angle (IS), in fuzzy logic. It is obtained by supplying the distance of obstacles from all directions and target distance from the robot's present location. Findings The present research is based on the navigation of humanoid NAO in complicated workspaces. Therefore, various simulations are performed in a 3D simulator in different complicated workspaces. The validation of their outcomes is done using the various experiments in similar workspaces using the proposed controller. The comparison between their outcomes demonstrates an acceptable correlation. Ultimately, evaluating the proposed controller with another existing navigation approach indicates a significant improvement in performance. Originality/value A new framework is developed to guide humanoid NAO in complicated workspaces, which is hardly seen in the available literature. Inspection in simulation and experimental workspaces verifies the robustness of the designed navigational controller. Considering minimum error ranges and near collaboration, the findings from both frameworks are evaluated against each other in respect of specified navigational variables. Finally, concerning other present approaches, the designed controller is also examined, and major modifications in efficiency have been reported.

scholarly journals A Genetic Algorithm with Fuzzy Crossover Operator and Probability

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammad Jalali Varnamkhasti ◽  
Lai Soon Lee ◽  
Mohd Rizam Abu Bakar ◽  
Wah June Leong
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Optimization Problems ◽  
Performance Measure ◽  
Population Diversity ◽  
Crossover Operator ◽  
Inference System ◽  
Combinatorial Optimization Problems ◽  
Fuzzy Genetic

The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions.

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.

Hybrid Neural-Fuzzy Controller for Motorized Robot Arm

2011 ◽  
Vol 403-408 ◽  
pp. 5068-5075
Author(s):  
Fatma Zada ◽  
Shawket K. Guirguis ◽  
Walied M. Sead
Keyword(s):  
Fuzzy Logic ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
System Response ◽  
Robot Arm ◽  
Neural Controller ◽  
Hybrid Controller ◽  
Neural Fuzzy ◽  
Transient And Steady State

In this study, a design methodology is introduced that blends the neural and fuzzy logic controllers in an intelligent way developing a new intelligent hybrid controller. In this design methodology, the fuzzy logic controller works in parallel with the neural controller and adjusting the output of the neural controller. The performance of our proposed controller is demonstrated on a motorized robot arm with disturbances. The simulation results shows that the new hybrid neural -fuzzy controller provides better system response in terms of transient and steady-state performance when compared to neural or fuzzy logic controller applications. The development and implementation of the proposed controller is done using the MATLAB/Simulink toolbox to illustrate the efficiency of the proposed method.

Fuzzy Hybrid Controller Model for Making Decision to Interpret Any Condition

2006 ◽  
Vol 111 ◽  
pp. 167-170
Author(s):  
M. Shahidul Karim ◽  
Rashed Mustafa
Keyword(s):  
Fuzzy Logic ◽  
Control Strategy ◽  
Room Temperature ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Electronic System ◽  
Comfort Level ◽  
Price Ratio ◽  
Hybrid Controller ◽  
Software Development System

The constantly increasing performance/price ratio of microcontrollers means electronic system can replace more and more electromechanical ones. In design, the goal is not to just replace the solution but also to improve it by adding new functionalities. The paper presents a model of industrial controller having possibility of the classical programming controller, with added elements of the fuzzy logic. Here fuzzy logic offers a technical control strategy that uses elements of everyday language. In this application, it is used to design a control strategy that adapts to the need of individual user. It achieves a higher comfort level and reduces energy consumption. Here we have used a fuzzy method which selects the contractions that best meet the specifications, where human knowledge is involved in a decision making process. With a fuzzy-logic software development system, the entire system, which includes conventional code for signal preprocessing as well as the fuzzy logic system, can be implemented on an industry-standard microcontroller. Using fuzzy logic on such a low-cost platform makes this a possible solution with most AC systems. Each home AC has a sensor that measures room temperature and compares it with the temperature set on the dial. The fuzzy logic controller uses a bimetallic switch and compares the set temperature with room temperature.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

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