Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

Anish Pandey,Dayal R.Parhi

Mechanical Engineering Department,N.I.T.,Rourkela,Orissa,India

Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

Anish Pandey*,Dayal R.Parhi

Mechanical Engineering Department,N.I.T.,Rourkela,Orissa,India

A R T I C L E I N F O

Article history:

Received 16 September 2016

Received in revised form

27 December 2016

Accepted 6 January 2017

Available online 7 January 2017

Singleton type-1 fuzzy

Navigation

Wind driven optimization

Membership function

Atmospheric motion

This article introduces a singleton type-1 fuzzy logic system(T1-SFLS)controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment.The WDO(Wind Driven Optimization)algorithm is used to optimize and tune the input/ output membership function parameters of the fuzzy controller.The WDO algorithm is working based on the atmospheric motion of in finitesimal small air parcels navigates over an N-dimensional search domain.The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot.As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.

©2017 Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license(http:// creativecommons.org/licenses/by-nc-nd/4.0/).

1.Introduction

‘Path planning and control’of an autonomous mobile robot in an unknown dynamic environment is one of the most challenging jobs.Fuzzy logic is a mimic of human behavior,which easily handles the system uncertainty.One of the most cited methods in the field of the mobile robot is the fuzzy logic.Soft computing techniques such as fuzzy logic[1],neural network[2],neuro-fuzzy[4] and nature-inspired algorithms(Genetic Algorithm[8],Particle Swarm Optimization[12,13],Ant Colony Algorithm[10,11],Simulated Annealing Algorithm[14,15],Bacterial Foraging Optimization [5])are widely used for mobile robot navigation.However,each method(algorithm)has its strengths and weaknesses.

The motion control problem of an autonomous wheeled mobile robot has been widely investigated in past two decades.Abadi and Khooban[1]have introduced Mamdani-type fuzzy logic controller integrated with random inertia weight Particle Swarm Optimization(RNW-PSO)for optimal path tracking of wheeled mobile robots(WMRs).Algabri et al.[2]have combined the fuzzy logic with other soft computing techniques such as Genetic Algorithm(GA), Neural Networks(NN),and Particle Swarm Optimization(PSO)for optimizing the membership function parameters of the fuzzy controller to improve the navigation performance of the mobile robot.A comparative study between two soft computing approaches,namely genetic-fuzzy and genetic-neural and the conventional potential field method have been designed and developed by Hui and Pratihar[3]for an adaptive navigation planning of a car-like mobile robot moving in the presence of some dynamic obstacles.Pothal and Parhi[4]have proposed the sensor based Adaptive Neuro Fuzzy Inference System(ANFIS)controller for navigation of single and multiple mobile robots in the highly cluttered environment.

Montiel et al.[5]and Hossain et al.[6]have explored the application of Bacterial Foraging Optimization(BFO)method in the field of mobile robot navigation to find out the shortest possible path within the minimum time to move from any start position to the goal position in an unknown environment between moving obstacles.Baturone et al.[7]have designed the low-cost embedded neuro-fuzzy controller for safe and collision-free navigation of an autonomous car-like robot among possible obstacles toward a goal configuration.Ming et al.[8]have designed a genetic algorithm to select the best membership functions from the fuzzy system to control a mobile robot in the partially unknown environment.Lianget al.[9]have presented the kinematic modeling of the twowheeled differential drive mobile robot.Purian and Sadeghian [10]have explored the optimal path for a mobile robot in an unknown environment using Ant Colony Optimization(ACO) algorithm.

To prepare an optimal intelligent controller for an autonomous wheeled mobile robot,the Castillo et al.[11]have designed the hybridization of an ACO algorithm and the PSO algorithm to optimize the membership function of a fuzzy controller.Chung et al. [12]have developed PSO and fuzzy control algorithm to navigate the robot in the unknown environment.Allawi and Abdalla[13] have proposed the sensor based PSO-fuzzy model for the navigation of multiple mobile robots.Where,the PSO is used to determine the optimal input/output membership functions and the optimal rules for the fuzzy type-2 controllers.Yanar and Akyurek[14]have proposed the use of simulated annealing metaheuristic algorithm for tuning the Mamdani type fuzzy models.Martinez-Alfaro and Gomez-Garcia[15]have developed the simulated annealing and fuzzy logic for generating an automatic path planning of the mobile robot.Mohanty and Parhi[20]have combined the cuckoo search algorithm with ANFIS for optimizing the navigation path length of mobile robots.Wong et al.[21]have used the PSO algorithm to tune the parameters of the membership function.

One major problem with the fuzzy logic is the dif ficulty of constructing and tuning the correct membership function grade [22].Therefore,the authors have tried to attempt to solve this problem by using WDO algorithm.In this article,a Fuzzy-WDO hybrid algorithm has been presented for mobile robot navigation and collision avoidance in an unknown static and dynamic environment.The WDO is integrated with the fuzzy controller to adjust and optimize the antecedent and consequent parameters of the generalized bell-shaped membership function.The WDO[16-18] method is a population-based iterative heuristic global optimization algorithm for multi-dimensional and multi-model problems with the potential to implement constraints on the search domain. This algorithm works by simultaneously maintaining several in finitesimal small air parcels or potential solutions in the search domain.For each iteration of the algorithm,each air parcels are evaluated by the membership function parameters(objective function)being optimized based on the fitness function of that solution.The primary objective of this research is to optimize the membership function parameters of the fuzzy controller by using WDO algorithm.

This article is organized into seven sections.Section 1 presents the introduction and literature review.T1-SFLS controller for mobile robot navigation is proposed in Section 2.The hybrid fuzzy-WDO algorithm for mobile robot navigation is presented in Section 3.Section 4 demonstrates the simulation results of the mobile robot in different environments.Section 5 describes the simulation result comparison with previous works.Section 6 presents the experimental results and discussion for validating the proposed controller.Finally,Section 7 depicts the summary.

Fig.1.The structure of a T1-SFLS controller for mobile robot navigation.

2.T1-SFLS controller for the mobile robot navigation

Fig.2.Fuzzy membership functions for the inputs(df,dl,and dr).

In this section,the T1-SFLS rule-based controller has been designed and implemented for mobile robot navigation andcollision avoidance in an unknown static and dynamic environment.The proposed controller controls the right motor velocity and left motor velocity of the mobile robot using sensory data interpretation.The T1-SFLS controller has three inputs:Forward Obstacle Distance(df),Left Forward Obstacle Distance(dl)and Right Forward Obstacle Distance(dr);and two outputs:Right Motor Velocity(mr)and Left Motor Velocity(ml),which are logically connected by eight rules(see Fig.1).The T1-SFLS controller receives inputs(obstacle distances)from the front,left,and the right group of sensors of the robot,and output from T1-SFLS controller is right motor velocity and left motor velocity of the mobile robot.These sensors read the obstacle from 20 cm to 150 cm approximately.The input and output variables of the controller are illustrated in Figs.2 and 3,respectively.The fuzzy rule set of the T1-SFLS controller is described in Table 1.The two generalized bellshaped(Gbell)membership functions are used for inputs and outputs.The range of inputs is divided into two linguistic variables: Near and Far.These inputs are located at 20 cm-150 cm.Similarly, the two Gbell membership functions(MFs)Low and High respectively have been used for the outputs,and it is located at 6.7 cm/s to 16.7 cm/s.The designed T1-SFLS controller is directly implemented in the mobile robot for simulations and experiments.The T1-SFLS controller is composed through Mamdani-type fuzzy model in the following form

Fig.3.Fuzzy membership functions for the outputs(mr,and ml).

Table 1Fuzzy rules set.

Fig.4.The general structure of the generalized bell-shaped membership function.

Table 2Adjusting parameters of the inputs before optimization.

Table 3Adjusting parameters of the outputs before optimization.

Then mris mr（ijk）and mlis ml（ijk）

where n=1,2,…,8(eight rules),the i=1,2,j=1,2 and k=1,2 because df,dland drhave two Gbell membership functions each. The df（i）,dl（j）,and dr（k）are the fuzzy sets of the inputs df,dl,and drrespectively.Similarly,the mr（ijk）,and ml（ijk）are the fuzzy sets of the outputs mr,and mlrespectively.The fuzzy set(inputs and outputs) uses the following Gbell membership function.

Fig.5.Air parcels representation of the WDO algorithm.

Let df,dl,and drare presented by x1,x2,and x3respectively. Similarly,mr,and mlare denoted by y1,and y2respectively.

where a,b,and c are adjusting parameters of the membership function;called as the half width,slope control,and center respectively.The general structure of the generalized bell-shaped membership function is shown in Fig.4.

The defuzzi fication of the outputs(y1and y2)are accomplished by the weighted average method

The adjusting parameters a,b,and c of the inputs and outputs are listed in Table 2 and Table 3,respectively,which will be optimized through the WDO algorithm in Section 3 below.

Fig.6.Fuzzy membership functions for the inputs(df,dl,and dr)after optimization.

3.Fuzzy-WDO algorithm for the mobile robot navigation

WDO[16]algorithm is inspired by the earth's atmosphere, where the wind blows are trying to equalize the horizontal imbalance in the air pressure.WDO is a new type nature-inspired global optimization based on atmospheric motion developed by Bayraktar et al.[16]in 2013.This method is working on the population-based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain.WDO is similar to other nature-inspired optimization algorithms,in which population-based heuristic iterative process can be found for solving multi-dimensional optimization problems[18].At itscenter,a population of in finitesimally small air parcels navigates over an N-dimensional search space,employing Newton's second law of motion that is used to express the motion of air parcels inside the earth's atmosphere.As compared to other particle based optimization algorithm(e.g.,PSO),the WDO algorithm has additional terms in the velocity update equation such as Gravitation and Coriolis forces,which provides robustness and extra degrees of freedom to the algorithm.

The WDO algorithm is working based on the atmospheric motion of in finitesimal small air parcels navigating over an N-dimensional search domain.The starting step of this algorithm is supported by the Newton's second law of motion,which provides accurate results when applied to the analysis of atmospheric motion.It states that the total force applied on an air parcel causes it to accelerate with an acceleration a in the same direction as the applied total force.

Fig.7.Fuzzy membership functions for the outputs(mr,and ml)after optimization.

Table 4Adjusting parameters of the inputs after optimization.

Table 5Adjusting parameters of the outputs after optimization.

whereρis the density of air for an in finitesimally small air parcel, and Firepresents all the individual forces acting on the air parcel.To relate the air pressure to the air parcel's density and temperature, the ideal gas law can be utilized and is given by

Fig.8.Mobile robot navigation between the obstacles using(a)T1-SFLS and(b)Fuzzy-WDO controller.

Fig.9.Mobile robot navigation between the walls using(a)T1-SFLS and(b)Fuzzy-WDO controller.

where P is the pressure,R is the universal gas constant,and T is the temperature.

Four major forces can be included in equation(9)that either cause the wind to move in a certain direction at a certain velocity or that de flect it from its existing path.The most observable force causing the air to move is the pressure gradient force FPGdefined in equation(11).Another force is the friction force FFdefined in equation(12),which simply acts to oppose the motion started by the pressure gradient force.In our three-dimensional physical atmosphere,the gravitational force FGin equation(13)is a vertical force directed toward the earth's surface.The Coriolis force FCin equation(14)is caused by due to the rotation of the earth and de flects the path of the wind from one dimension to another.

where,∇P is the pressure gradient,δV represents the in finite air volume,Ωrepresents the rotation of the earth,g is the gravitational acceleration,αis the friction coef ficient and u is the velocity vector of the wind.

The sum of all forces(gravitational force,pressure gradient force,friction force,and Coriolis force)described above can be entered on the right-hand side of Newton's second law of motion given in equation(9),which leads to

where the acceleration term in equation(9)is rewritten as a=Δu/Δt,and a time stepΔt=1 is assumed for simplicity.For an in finitesimally small,dimensionless air parcel,the volume is set as δV=1,which simpli fies equation(15)to Putting the ideal gas law equation(10)in equation(16),the densityρcan be written in terms of the pressure,with temperature and the universal gas law constant

where xnewis the new position of the air parcel in the next iteration. If the new velocity unewexceeds the initialize maximum velocity (umax=0.3)in any dimension,then the velocity in that dimension is limited according to the following condition

where the direction of motion is preserved but the magnitude is limited to be no more than|umax|at any dimension and u*newrepresents the adjusted velocity after it is limited to the maximum speed.

The pseudo-code of the WDO algorithm can be summarized as follows:

Step 1.Start.

Step 2.Initialize the population size(i.e.,group of air parcels), number of dimensions of the optimization problem,maximum number of iterations,coef ficients(such as RT,g,α,c,umax), pressure function(fitness function of the optimization problem),lower and upper boundaries of the optimization problem.

Fig.10.Mobile robot navigation in the dynamic environment using Fuzzy-WDO controller.

Table 6The simulation results of T1-SFLS and Fuzzy-WDO controllers.

Step 3.Assign random position and velocity of the air parcels. Step 4.Evaluate the pressure(fitness)values of each air parcel at its current position.

Step 5.Once the pressure values have been evaluated,the population is ranked based on their pressure(ascending order),and the velocity updated according to equation(17)along with the restrictions are given in equation(19).

Step 6.Update the position of the air parcel for the next iteration according to equation(18)and also check the boundaries of the air parcel.

Step 7.Stop if a maximumnumber of iterations are achieved,else go to step 4.

When the maximum number of iterations is completed,the best pressure(fitness)value is achieved.

Fig.11.Mobile robot navigation in an environment without obstacle using fuzzy controller[23].

Fig.12.Mobile robot navigation in an environment without obstacle using Fuzzy-WDO controller.

Fig.13.Mobile robot navigation in an environment with four obstacles using fuzzy controller[23].

Table 7The simulation result comparison between the fuzzy controller[23]and proposed Fuzzy-WDO controller.

Fig.15.Mobile robot navigation between many obstacles using fuzzy model[24].

Fig.16.Mobile robot navigation between many obstacles using Fuzzy-WDO controller.

Table 8Comparison of simulation results between fuzzy model[24]over proposed Fuzzy-WDO controller.

This section describes the WDO algorithm used for the membership function parameter optimization of the T1-SFLS controller for the optimum navigation and collision avoidance in an unknown static and dynamic environment.One major problem with the fuzzy logic is the dif ficulty of constructing and tuning thecorrect membership function grade[22].Because of this problem, the WDO algorithm is used to tune the adjusting parameters of the inputs and outputs.From Section 2,two Gbell membership function are considered for the inputs(df,dl,and dr)and outputs(mr, and ml).Each Gbell membership function has three adjusting parameters(a,b,and c).Therefore,each input has six adjusting parameters.Similarly,each output has six adjusting parameters.So the total number of adjusting parameters is to be thirty{5(3 inputs+2 outputs)×2(membership function)×3(adjusting parameters a,b,and c)=30}.

The ranges of adjusting parameters are defined as[a min,a max] [bmin,bmax]and[cmin,cmax]respectively,for lower and the upper boundary of the WDO algorithm.The aminand amaxare 30 and 65 for the membership function of the inputs.The bminand bmaxare 1 and 3.5 for the membership function of the inputs.The parameters cminand cmaxare 20 and 150 for the membership function of inputs respectively.Similarly,the aminand amaxare 2 and 5 for the membership function of outputs.The bminand bmaxare 1 and 3.5 for the membership function of the outputs.The parameters cminand cmaxare located at 6.7 and 16.7 for the membership function of outputs respectively.Fig.5 shows the air parcels representation ofthe WDO algorithm.The optimized membership functions of the inputs(df,dl,and dr)and the outputs(mr,and ml)are shown in Figs.6 and 7,respectively.The results of the adjusting parameters (a,b,and c)of the inputs and outputs after optimization are listed in Table 4 and Table 5,respectively.

Fig.17.Infrared proximity sensor distribution of Khepera-III mobile robot.

Fig.18.Real-time navigation of Khepera-III mobile robot between the obstacles using T1-SFLS and Fuzzy-WDO controller.

The most important step in applying the WDO algorithm is to select the fitness function,which is used to evaluate the optimum pressure of the air parcels.In during the optimization process,the combined root mean square errors(CRMSE)are used to evaluate the fitness of the fuzzy controller

4.Simulation results

This section describes the successful simulation results using T1-SFLS and Fuzzy-WDO controllers in the various unknown static and dynamic environments.The simulations are conducted using the MATLAB software on the HP 3.40 GHz processor.Figs.8 and 9 show the navigation result of the mobile robot between the obstacles and walls respectively,using the T1-SFLS and Fuzzy-WDO controller in the unknown environments.Similarly,Fig.10 demonstrates the navigation of a mobile robot in an unknown environment with the presence of two dynamic obstacles using Fuzzy-WDO controller.It is assumed that the position of the start point and goal point are known.But the positions of all the obstacles in the environment are unknown for the robot.In the simulation results,the green and red color trajectory indicates the path generated by the T1-SFLS and Fuzzy-WDO controllers respectively. Simulation results show the Fuzzy-WDO controller gives smooth and optimal path compared to the T1-SFLS controller.Table 6 shows the navigation path length and time taken by the robot using the T1-SFLS and Fuzzy-WDO controller in the various unknown environments.

5.Comparison with previous works

This section describes the computer simulation result comparison between the previous model[23]and proposed Fuzzy-WDO controller in the same path planning problems.In the article[23], the authors have used two simple fuzzy controllers such as tracking fuzzy logic control(TFLC)and obstacle avoidance fuzzy logic control(OAFLC)without adjusting its membership function for mobile robot navigation.Figs.11 and 12 show the mobile robot navigation in the same environment without obstacle using fuzzy controller [23]and proposed Fuzzy-WDO controller,respectively.Similarly, Figs.13 and 14 present the path covered by the robot in the same environment with the four obstacles using fuzzy controllers[23] and proposed Fuzzy-WDO controller,respectively.From the simulation figures,it can be seen that the proposed Fuzzy-WDO controller covers shorter distance to reach the goal as compared to previous model[23]because WDO algorithm adjusts the membership function of the fuzzy controller,which provides better result compared to the standalone fuzzy model.Besides,the proposed Fuzzy-WDO controller also helps the mobile robot to reach the goal without taking any intermediate point.And due to this,it takes less time to reach the goal as compared to previous model [23].Table 7 illustrates the path traced(in cm)by the robot to reach the goal using proposed controller and previous model[23].

In an article[24],the Cherroun and Boumehraz have designed the behaviour based Takagi-Sugeno type fuzzy model,which autonomously navigates the mobile robot in the crowded environment.Figs.15 and 16 show the mobile robot navigation result comparison between the fuzzy model[24]and proposed Fuzzy-WDO controller,respectively.From the simulation results,it can be seen that the proposed controller provides the better trajectory in terms of path length and smoothness as compared to previous model.Table 8 shows the path covered(in cm)by the robot to reach the goal using proposed controller and previous model[24].The centimetre measurements are taken on the proportional basis.

6.Experimental results

6.1.Khepera-III mobile robot description

The experiments are conducted using the Khepera-III mobile robot in unknown environments.The Khepera-III mobile robot has two wheels controlled by two DC servo motors and one caster wheel.The diameter and height of the robot are 13 cm and 7 cm respectively.The Khepera-III mobile robot is equipped with nine infrared proximity sensors and five ultrasonic sensors,as shown in Fig.17.The Infrared proximity sensor reads obstacles up to 30 cm, and the ultrasonic sensor reads obstacles from 20 cm to 4 m approximately.In this study,we have set the minimum and maximum velocity of Khepera-III mobile robot between the 6.7-16.7 cm/s.

6.2.Experiments

In the experiments,the controllers are implemented in the Khepera-III mobile robot using HP laptop.The width and height of the experimental platform are 250 cm and 250 cm,respectively. Fig.18 and Fig.19 shows the real-time navigation of the Khepera-III mobile robot in unknown environments with the obstacles and walls,respectively.In Fig.18,the start position of the robot is(175, 100)cm,and the position of the goal is(0,200)cm.The starting angle between the robot and the goal is 29.74°.Similarly,in Fig.19, the start position of the robot is(50,50)cm,and the goal position is (250,200)cm.The starting angle between the robot and the goal is 36.87°.In the experiments,it is assumed that the position of the start point and goal point are known,but the positions of all the obstacles in the environment are unknown for the robot.The T1-SFLS and Fuzzy-WDO controller generate the motor velocity control command for obstacle avoidance using on-board sensor information.The successful experimental results in the various unknown environments are shown below to verify the effectiveness of the T1-SFLS and Fuzzy-WDO controllers.Table 9 shows the experimental path length and time taken by the Khepera-III mobile robot to reach target using the T1-SFLS and Fuzzy-WDO controllers in the various unknown environments.Tables 10 and 11 present the traveling path length and navigation time comparison between the simulation and experimental results.In the comparison study between the simulation and experiments,it is observed that some errors have been found,these happen due to slippage and friction during real time experiment.

Fig.19.Real-time navigation of Khepera-III mobile robot between the walls using T1-SFLS and Fuzzy-WDO controller.

Table 9The experimental results of T1-SFLS and Fuzzy-WDO controllers.

7.Conclusion

In this article,the two methods T1-SFLS controller and the hybrid Fuzzy-WDO algorithm have been applied to the mobile robot navigation.A new population-based optimization algorithm, called Wind Driven Optimization(WDO)is used to optimize and set the antecedent and consequent parameters of the fuzzy controller. The proposed algorithms are successfully verified through simulations and real-time experiments in the different environments. Simulation and experimental results demonstrate the Fuzzy-WDO controller provide better performance as compared to the T1-SFLS controller.

Table 10Traveling path lengths comparison between simulation and experimental results.

Table 11Navigation time comparison between simulation and experimental results.

[1]Abadi DNM,Khooban MH.Design of optimal mamdani-type fuzzy controller for nonholonomic wheeled mobile robots.J King Saud University-Engineering Sci 2015;27(1):92-100.

[2]Algabri M,Mathkour H,Ramdane H,Alsulaiman M.Comparative study of soft computing techniques for mobile robot navigation in an unknown environment.Comput Hum Behav 2015;50:42-56.

[3]Hui NB,Pratihar DK.A comparative study on some navigation schemes of a real robot tackling moving obstacles.Robotics Computer-Integrated Manuf 2009;25(4):810-28.

[4]Pothal JK,Parhi DR.Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system.Robotics Aut Syst 2015;72:48-58.

[5]Montiel O,Orozco-Rosas U,Sepulveda R.Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 2015;42(12):5177-91.

[6]Hossain MA,Ferdousand I.Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique.Robotics Aut Syst 2015;64:137-41.

[7]Baturone I,Gersnoviez A,Barriga A.Neuro-Fuzzy techniques to optimize an FPGA embedded controller for robot navigation.Appl Soft Comput 2014;21: 95-106.

[8]Ming L,Zailin G,Shuzi Y.Mobile robot fuzzy control optimization using genetic algorithm.Artif Intell Eng 1996;10(4):293-8.

[9]Liang Y,Xu L,Wei R,Hu H.Adaptive fuzzy control for trajectory tracking of mobile robot.In:IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS),2010,pp.4755-4760.

[10]Purian FK,Sadeghian E.Mobile robots path planning using ant colony optimization and fuzzy logic algorithms in unknown dynamic environments.In: IEEE International Conference on Control,Automation,Robotics and Embedded Systems(CARE),2013,pp.1-6.

[11]Castillo O,Martinez-Marroquin R,Melin P,Valdez F,Soria J.Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot.Inf Sci 2012;192: 19-38.

[12]Chung HY,Hou CC,Liu SC.Automatic navigation of a wheeled mobile robot using particle swarm optimization and fuzzy control.In:IEEE International Symposium on Industrial Electronics(ISIE),2013,pp.1-6.

[13]Allawi ZT,Abdalla TY.A PSO-optimized type-2 fuzzy logic controller for navigation of multiple mobile robots.In:IEEE International Conference on Methods and Models in Automation and Robotics(MMAR),2014,pp.33-39. [14]Yanar TA,Akyurek Z.Fuzzy model tuning using simulated annealing.Expert Syst Appl 2011;38(7):8159-69.

[15]Martinez-Alfaro H,Gomez-Garcia S.Mobile robot path planning and tracking using simulated annealing and fuzzy logic control.Expert Syst Appl 1998;15(3):421-9.

[16]Bayraktar Z,Komurcu M,Bossard JA,Werner DH.The wind driven optimization technique and its application in electromagnetics.IEEE Trans Antennas Propag 2013;61(5):2745-57.

[17]Kuldeep B,Singh VK,Kumar A,Singh GK.Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.ISA Trans 2015;54:101-16.

[18]Bhandari AK,Singh VK,Kumar A,Singh GK.Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy.Expert Syst Appl 2014;41(7): 3538-60.

[19]Bayraktar Z,Turpin JP,Werner DH.Nature-inspired optimization of highimpedance metasurfaces with ultrasmall interwoven unit cells.IEEE Antennas Wirel Propag Lett 2011;10:1563-6.

[20]Mohanty PK,Parhi DR.A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach.Memetic Comput 2015;7(4):255-73.

[21]Wong C,Wang H,Li S.PSO-based motion fuzzy controller design for mobile robots.Int J fuzzy Syst 2008;10(1):284-92.

[22]Tahmasebi P,Hezarkhani A.A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation.Comput Geosci 2014;42:18-27.

[23]Faisal M,Hedjar R,Al Sulaiman M,Al-Mutib K.Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. Int J Adv Robotic Syst 2013;10(37):1-7.

[24]Cherroun L,Boumehraz M.Fuzzy behavior based navigation approach for mobile robot in unknown environment.J Electr Eng 2013;13(4):1-8.

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Submission to this journal proceeds totally online. Use the following guidelines to prepare your article. Via the homepage of this journal ( http://ees.elsevier.com/xxx) you will be guided stepwise through the creation and uploading of the various files. The system automatically converts source files to a single Adobe Acrobat PDF version of the article, which is used in the peer-review process. Please note that even though manuscript source files are converted to PDF at submission for the review process, these source files are needed for further processing after acceptance. All correspondence, including notification of the Editor’s decision and requests for revision, takes place by e-mail and via the author’s homepage, removing the need for a hard-copy paper trail. If you are unable to provide an electronic version, please contact the editorial office prior to submission (e-mail: defence1@cn-bgxh.cn; telephone: 10-86-68964830; or fax: 10-86-68963025).

Additional Information

Tables and figures may be presented with captions within the main body of the manuscript; if so, figures should additionally be uploaded as high resolution files.

PREPARATION

Use of wordprocessing software

It is important that the file be saved in the native format of the wordprocessor used. The text should be in single-column format. Keep the layout of the text as simple as possible. Most formatting codes will be removed and replaced on processing the article. In particular, do not use the wordprocessor’s options to justify text or to hyphenate words. However, do use bold face, italics, subscripts, superscripts etc. When preparing tables, if you are using a table grid, use only one grid for each individual table and not a grid for each row. If no grid is used, use tabs, not spaces, to align columns. The electronic text should be prepared in a way very similar to that of conventional manuscripts (see also the Guide to Publishing with Elsevier: http://www.elsevier.com/guidepublication). Note that source files of figures, tables and text graphics will be required whether or not you embed your figures in the text. See also the section on Electronic illustrations.

To avoid unnecessary errors you are strongly advised to use the “spellcheck” and “grammar-check” functions of your wordprocessor.

Latex

If the LaTeX file is suitable, proofs will be produced without rekeying the text. The article should preferably be written using Elsevier’s document class“elsarticle”, or alternatively any of the other recognized classes and formats supported in Elsevier’s electronic submissions system, for further information see http://www.elsevier.com/wps/find/authorsview.authors/latexees-supported. The Elsevier “elsarticle” LaTeX style file package (including detailed instructions for LaTeX preparation) can be obtained from the Quickguide: http://www.elsevier.com/latex. It consists of the file: elsarticle. cls, complete user documentation for the class file, bibliographic style files in various styles, and template files for a quick start.

Article structure

Subdivision - numbered sections

Divide your article into clearly defined and numbered sections. Subsections should be numbered 1.1 (then 1.1.1, 1.1.2, ...), 1.2, etc. (the abstract is not included in section numbering). Use this numbering also for internal crossreferencing: do not just refer to “the text”. Any subsection may be given a brief heading. Each heading should appear on its own separate line.

Introduction

State the objectives of the work and provide an adequate background, avoiding a detailed literature survey or a summary of the results.

Material and methods

Provide sufficient detail to allow the work to be reproduced. Methods already published should be indicated by a reference: only relevant modifications should be described.

Theory/calculation

A Theory section should extend, not repeat, the background to the article already dealt with in the Introduction and lay the foundation for further work. In contrast, a Calculation section represents a practical development from a theoretical basis.

Results

Results should be clear and concise.

Discussion

This should explore the significance of the results of the work, not repeat them. A combined Results and Discussion section is often appropriate. Avoid extensive citations and discussion of published literature.

Conclusions

The main conclusions of the study may be presented in a short Conclusions section, which may stand alone or form a subsection of a Discussion or Results and Discussion section.

Appendices

If there is more than one appendix, they should be identified as A, B, etc. Formulae and equations in appendices should be given separate numbering: Eq. (A1), Eq. (A2), etc.; in a subsequent appendix, Eq. (B1) and so on. Similarly for tables and figures: Table A1; Fig. A1, etc.

Essential title page information

• Title.Concise and informative. Titles are often used in information-retrieval systems. Avoid abbreviations and formulae where possible.

• Author names and affiliations.Where the family name may be ambiguous (e.g., a double name), please indicate this clearly. Present the authors’ affiliation addresses (where the actual work was done) below the names. Indicate all affiliations with a lower-case superscript letter immediately after the author’s name and in front of the appropriate address. Provide the full postal address of each affiliation, including the country name, and, if available, the e-mail address of each author.

• Corresponding author.Clearly indicate who will handle correspondence at all stages of refereeing and publication, also post-publication. Ensure thattelephone and fax numbers (with country and area code) are provided in addition to the e-mail addr ess and the complete postal addr ess. Contact details must be kept up to date by the corresponding author.

• Present/permanent address.If an author has moved since the work described in the article was done, or was visiting at the time, a “Present address” (or “Permanent address”) may be indicated as a footnote to that author’s name. The address at which the author actually did the work must be retained as the main, affiliation address. Superscript Arabic numerals are used for such footnotes.

Abstract

A concise and factual abstract is required. The abstract should state briefly the purpose of the research, the principal results and major conclusions. An abstract is often presented separately from the article, so it must be able to stand alone. For this reason, References should be avoided, but if essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself.

Keywords

Authors are invited to submit keywords associated with their paper.

Abbreviations

Define abbreviations that are not standard in this field in a footnote to be placed on the first page of the article. Such abbreviations that are unavoidable in the abstract must be defined at their first mention there, as well as in the footnote. Ensure consistency of abbreviations throughout the article.

Acknowledgements

Collate acknowledgements in a separate section at the end of the article before the references and do not, therefore, include them on the title page, as a footnote to the title or otherwise. List here those individuals who provided help during the research (e.g., providing language help, writing assistance or proof reading the article, etc.).

Nomenclature and units

Follow internationally accepted rules and conventions: use the international system of units (SI). If other quantities are mentioned, give their equivalent in SI. Authors wishing to present a table of nomenclature should do so on the second page of their manuscript.

Math formulae

Present simple formulae in the line of normal text where possible and use the solidus (/) instead of a horizontal line for small fractional terms, e.g., X/Y. In principle, variables are to be presented in italics. Powers of e are often more conveniently denoted by exp. Number consecutively any equations that have to be displayed separately from the text (if referred to explicitly in the text).

Footnotes

Footnotes should be used sparingly. Number them consecutively throughout the article, using superscript Arabic numbers. Many wordprocessors build footnotes into the text, and this feature may be used. Should this not be the case, indicate the position of footnotes in the text and present the footnotes themselves separately at the end of the article. Do not include footnotes in the Reference list.

Table footnotes

Indicate each footnote in a table with a superscript lowercase letter.

Artwork

Electronic artwork

General points

• Make sure you use uniform lettering and sizing of your original artwork.

• Save text in illustrations as “graphics” or enclose the font.

• Only use the following fonts in your illustrations: Arial, Courier, Times, Symbol.

• Number the illustrations according to their sequence in the text.

• Use a logical naming convention for your artwork files.

• Provide captions to illustrations separately.

• Produce images near to the desired size of the printed version.

• Submit each figure as a separate file.

A detailed guide on electronic artwork is available on our website: http:// www.elsevier.com/artworkinstructions

You ar e ur ged to visit this site; some excer pts fr om the detailed information are given here.

Formats

Regardless of the application used, when your electronic artwork is fi-nalised, please “save as” or convert the images to one of the following formats (note the resolution requirements for line drawings, halftones, and line/halftone combinations given below):

EPS: Vector drawings. Embed the font or save the text as “graphics”.

TIFF: color or grayscale photographs (halftones): always use a minimum of 300 dpi.

TIFF: Bitmapped line drawings: use a minimum of 1000 dpi.

TIFF: Combinations bitmapped line/half-tone (color or grayscale): a minimum of 500 dpi is required.

If your electronic artwork is created in a Microsoft Office application (Word, PowerPoint, Excel) then please supply “as is”.

Please do not:

• Supply files that are optimised for screen use (like GIF, BMP, PICT, WPG); the resolution is too low;

• Supply files that are too low in resolution;

• Submit graphics that are disproportionately large for the content.

Color artwork

Please make sure that artwork files are in an acceptable format (TIFF, EPS or MS Office files) and with the correct resolution. If, together with your accepted article, you submit usable color figures then Elsevier will ensure, at no additional charge, that these figures will appear in color on the Web (e.g., ScienceDirect and other sites) regardless of whether or not these illustrations are reproduced in color in the printed version.

Figure captions

Ensure that each illustration has a caption. Supply captions separately, not attached to the figure. A caption should comprise a brief title (not on the figure itself) and a description of the illustration. Keep text in the illustrations themselves to a minimum but explain all symbols and abbreviations used.

Tables

Number tables consecutively in accordance with their appearance in the text. Place footnotes to tables below the table body and indicate them with superscript lowercase letters. Avoid vertical rules. Be sparing in the use of tables and ensure that the data presented in tables do not duplicate results described elsewhere in the article.

References

Please ensure that every reference cited in the text is also present in the reference list (and vice versa). Any references cited in the abstract must be given in full. Unpublished results and personal communications are not recommended in the reference list, but may be mentioned in the text. If these references are included in the reference list they should follow the standard reference style of the journal and should include a substitution of the publication date with either “Unpublished results” or “Personal communication”Citation of a reference as “in press” implies that the item has been accepted for publication.

Web references

As a minimum, the full URL should be given and the date when the reference was last accessed. Any further information, if known (DOI, author names, dates, reference to a source publication, etc.), should also be given. Web references can be listed separately (e.g., after the reference list) under a different heading if desired, or can be included in the reference list.

References in a special issue

Please ensure that the words ‘this issue’ are added to any references in the list (and any citations in the text) to other articles in the same Special Issue. Reference management software

This journal has standard templates available in key reference management packages EndNote (http://www.endnote.com/support/enstyles.asp) and Reference Manager (http://refman.com/support/rmstyles.asp). Using plugins to wordprocessing packages, authors only need to select the appropriate journal template when preparing their article and the list of references and citations to these will be formatted according to the journal style which is described below.

Reference style

Text: All citations in the text should refer to:

[1] Each reference is identified using a superscripted number and is placed after punctuation Single author: the author’s name (without initials, unless there is ambiguity) and the year of publication;

[2] References are numbered consecutively in order of appearance in the text;

[3] Multiple references are separated by closed-up commas and en dash used for ranges.

[4] References cited in tables or figure legends should be included in sequence at the point where the table or figure is first mentioned in the text

[5] Abstracts should not be cited unless it is the only available reference to an important concept

[6] Uncompleted work or work that has not yet been accepted for publication (i.e. “unpublished observation”, “personal communication”) should not be cited as references

[7] If reference cited only has 2 authors, both surnames are listed, e.g. Hawkins and Price reported that

[8] If only 1 author, then: Hawkins reported that

[9] If ≥ 3 authors, then: Hawkins et al reported that… [note: “et al” has no end period and is not in italics

Reference to a journal publication:

Vasiliy S, Kyle T, Paul J. Reentry time prediction using atmospheric density corrections. Journal of Guidance, Control, and Dynamics, 2008, 31(2): 282-289.

Van der Geer J, Hanraads J A J, Lupton R A. The art of writing a scientific article. Journal of Scientific Communications , 2000,163:51-59.

Reference to a book:

Strunk Jr W, White E B. The Elements of Style, third ed. Macmillan, New York, 1979.

Reference to a chapter in an edited book:

Mettam G R, Adams L B. How to prepare an electronic version of your article. in: Jones B S, Smith R Z ,eds. Introduction to the Electronic Age. E-Publishing Inc. New York, 1999: 281-304.

Journal abbreviations source

Journal names should be abbreviated according to; Index Medicus journal abbreviations: http://www.nlm.nih.gov/tsd/serials/lji.html; List of title word abbreviations: http://www.issn.org/2-22661-LTWA-online.php; CAS (Chemical Abstracts Service): http://www.cas.org/sent.html.

Submission checklist

The following list will be useful during the final checking of an article prior to sending it to the journal for review. Please consult this Guide for Authors for further details of any item.

Ensure that the following items are present:

One Author designated as corresponding Author:

• E-mail address

• Full postal address

• Telephone and fax numbers

All necessary files have been uploaded:

• Keywords

• All figure captions

• All tables (including title, description, footnotes)

Further considerations

• Manuscript has been “spellchecked” and “grammar-checked”

• References are in the correct format for this journal

• All references mentioned in the Reference list are cited in the text, and vice versa

• Permission has been obtained for use of copyrighted material from other sources (including the Web)

• Color figures are clearly marked as being intended for color reproduction on the Web (free of charge) and in print or to be reproduced in color on the Web (free of charge) and in black-and-white in print

• If only color on the Web is required, black and white versions of the figures are also supplied for printing purposes

For any further information please visit our customer support site at http:// support.elsevier.com.

AFTER ACCEPTANCE

Use of the Digital Object Identif er

The Digital Object Identifier (DOI) may be used to cite and link to electronic documents. The DOI consists of a unique alpha-numeric character string which is assigned to a document by the publisher upon the initial electronic publication. The assigned DOI never changes. Therefore, it is an ideal medium for citing a document, particularly ‘Articles in press’because they have not yet received their full bibliographic information. The correct format for citing a DOI is shown as follows (example taken from a document in the journal Physics Letters B): doi:10.1016/j.physletb .2010.09.059

When you use the DOI to create URL hyperlinks to documents on the web, they are guaranteed never to change.

Proofs

One set of page proofs (as PDF files) will be sent by e-mail to the corresponding author (if we do not have an e-mail address then paper proofs will be sent by post) or, a link will be provided in the e-mail so that authors can download the files themselves. Elsevier now provides authors with PDF proofs which can be annotated; for this you will need to download Adobe Reader version 7 (or higher) available free from http:// get.adobe.com/reader. Instructions on how to annotate PDF files will accompany the proofs (also given online). The exact system requirements are given at the Adobe site: http://www.adobe.com/products/reader/systemreqs.

If you do not wish to use the PDF annotations function, you may list the corrections (including replies to the Query Form) and return them to Elsevier in an e-mail. Please list your corrections quoting line number. If, for any reason, this is not possible, then mark the corrections and any other comments (including replies to the Query Form) on a printout of your proof and return by fax, or scan the pages and e-mail, or by post. Please use this proof only for checking the typesetting, editing, completeness and correctness of the text, tables and figures. Significant changes to the article as accepted for publication will only be considered at this stage with permission from the Editor. We will do everything possible to get your article published quickly and accurately - please let us have all your corrections within 48 hours. It is important to ensure that all corrections are sent back to us in one communication: please check carefully before replying, as inclusion of any subsequent corrections cannot be guaranteed. Proofreading is solely your responsibility. Note that Elsevier may proceed with the publication of your article if no response is received.

AUTHOR INQUIRIES

For inquiries relating to the submission of articles (including electronic submission) please visit this journal’s homepage. Contact details for questions arising after acceptance of an article, especially those relating to proofs, will be provided by the publisher. You can track accepted articles at http://www.elsevier.com/trackarticle. You can also check our Author FAQs ( http://www.elsevier.com/authorFAQ) and/or contact Customer Support via http://support.elsevier.com.

in text

*Corresponding author.

E-mail addresses:anish06353@gmail.com(A.Pandey),dayalp3@yahoo.in (D.R.Parhi).

Peer review under responsibility of China Ordnance Society.