literature review on obstacle avoidance robot

0000298379 00000 n The antecedent and consequent parts of the fuzzy controller have aligned by the fuzzy type-2 clustering and ACO respectively. PSO algorithm is used to find an optimal or near optimal solution of the problem using fitness function, where is a population of the particles. Implementation of Human–Like Driving Skills by Autonomous Fuzzy Behavior Control on an FPGA–Based Car–Like Mobile Robot. 0000299019 00000 n Motion Planning in a Plane Using Generalized Voronoi Diagrams. Mobile robot navigation and obstacle avoidance techniques: A review. 0000015399 00000 n 0000243611 00000 n 0000014881 00000 n Robot Path Planning of a Mobile Robot using Solid Modeling Techniques on Potential Fields. +b ��5�M�"`l�@��>��M��B� N��b T��.�.�CB�� v�y@K +��_��m�˲'�W��;Mj0�w��t��(�V��)�j��C0 ��@4�t4ރ��:QYq�i�JF|��o\4t��JB2w*d��L2�D�' Iqq��OM�x 5�\��inʰ��Ep". 0000317466 00000 n I had enough time to check the paper. 0000177269 00000 n Section 3 discusses the literature review of various soft computing techniques used for mobile robot navigation. startxref Obstacle avoidance Robot is designed in order to navigate the robot in unknown environment by avoiding collisions. 0000295468 00000 n Evolutionary–Group–based Particle–Swarm Optimized Fuzzy Controller with Application to Mobile–Robot Navigation in Unknown Environments. Deshpande & Bhosale84 have discussed the navigation of a nonholonomic wheeled mobile robot using ANFIS controller. Pratihar et al.44 have developed a genetic-fuzzy technique based on a combined approach of genetic algorithm and fuzzy logic (GA-FL) to solve the mobile robot motion planning problems in the dynamic environments. The motion problem of the wheeled mobile robots on uneven terrain has been addressed in Chakraborty.19 Wang & Yang20 have developed the neuro-fuzzy controller for navigation of a nonholonomic differential drive mobile robot. LITERATURE SURVEY llower and obstacle avoidance bot using arduinoâ has been designed and developed by Aamir attar, Aadilansari, Abhishekdesai, Shahid khan, Dipashrisonawale to create an autonomous robot which intelligently detects the obstacle in its path and navigates according to the actions that user set for it. Fuzzy Logic Control of an Autonomous Mobile Robot. 0000246379 00000 n 0000300486 00000 n PSO algorithm has been successfully applied by Raja & Pugazhenthi123 to optimize the travel time of the mobile robot in the dynamic environments. 0000010758 00000 n The behavior-based fuzzy logic controller has been made by Dongshu et al.49 to solve the navigation problem of mobile robot in unknown dynamic environment. 0000184295 00000 n Muthu T, Thierry Gloude R, Swaminathan S, et al. Optimal Path Planning for Mobile Robots Based on Intensified Ant Colony Optimization Algorithm. Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing. Mobile Robot Fuzzy Control Optimization Using Genetic Algorithm. 0000424891 00000 n Comparative Study of Soft Computing Techniques for Mobile Robot Navigation in an Unknown Environment. 2013;4(2):137–140. To design a robotic car capable of obstacle detection in a controlled environment. OK 73034 (Mailing Address) More Locations, Roosevelt 7/ 8. Liang Y, Xu L, Wei R, et al. Mahmud et al.66 have presented the vision (camera) sensor based Kohonen-type artificial neural network for intelligent navigation of mobile robot. Zhang & Li122 have presented a new objective function for mobile robot navigation using PSO. 0000013991 00000 n 0000298804 00000 n Guan-Zheng et al.135 have presented the modern global path planning method for a mobile robot by applying Ant Colony System (ACS) algorithm and the Dijkstra algorithm. Arora et al.103 have presented the single fitness based genetic algorithm for solving the navigation problem in the dynamic environments. 0000016352 00000 n 0000298271 00000 n In this mode robot is made to move in an obstacle free path i.e. Yang & Meng56 have applied the biologically inspired neural network to generate a collision-free path in a nonstationary environment. In recent years, there has been a growing interest in the design and development of an autonomous wheeled mobile robot using various soft computing techniques. Bi Z, Yimin Y, Yisan X. 0000013832 00000 n The eight rule-based fuzzy controllers have been designed by Boubertakh et al.29 for obstacle avoidance and goal-seeking behavior of the mobile robot. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. 0000324881 00000 n Obstacle Avoiding Robot Literature Review, curriculum vitae sample for seafarer, contoh teks recount beserta soal essay dan jawabannya, a sample of an application letter for a teaching job. Hussein A, Mostafa H, Badrel–din M, Sultan O, Khamis A (2012) Metaheuristic Optimization Approach to Mobile Robot Path Planning. Due to the slow convergence rate of the conventional simulated annealing algorithm, the Liang & Xu112 have presented a modified simulated annealing algorithm, and applied it to mobile robot global path planning. Demirli K, Khoshnejad M. Autonomous Parallel Parking of a Car–Like Mobile Robot by a Neuro–Fuzzy Sensor–Based Controller. The sensor-based mobile robot navigation in an indoor environment using a fuzzy logic controller has been discussed.31,32 Wu et al.33 have developed the sensor based mobile robot navigation in the narrow environment using fuzzy controller and genetic algorithm. 0000295251 00000 n Download your paper Live Chat. The detection of obstacle, halting of robot, rotation of servo motor to rotate the ultrasonic sensor, measurement of distance on left and right side and turning of robot around the obstacle are all managed by the Arduino sketch. Heuristic Fuzzy–Neuro Network and its Application to Reactive Navigation of a Mobile Robot. It will use an ultrasonic distance sensor and a servo motor in addition to the basic robot. In the past few years, many soft computing techniques are proposed by the researchers to solve the robot navigation and obstacle avoidance problem in the various environments. 2524 N. Broadway They have used PSO algorithm to escape the robot from the dead-end condition, and the fuzzy algorithm is used to control the turn angle of a wheeled mobile robot during navigation and obstacle avoidance. To evaluate literature related to the fields of engineering, mechatronics, and software development in the design, construction, and programming of an autonomous robot. 0000244073 00000 n 0000047821 00000 n 0000012222 00000 n Many methods have been developed for global navigation, i.e. Al Mutib K, Mattar E. Neuro–fuzzy Controlled Autonomous Mobile Robotics System. 0000013779 00000 n 0000327512 00000 n Selekwa et al.43 have presented the fuzzy behavior controller for mobile robot navigation in the densely obstacle populated environments. Intelligent Path Planning Algorithm For Autonomous Robot Based on Recurrent Neural Networks. IEEE J. In the local navigation, the robot can decide or control its motion and orientation autonomously using equipped sensors such as ultrasonic range finder sensors, sharp infrared range sensors, and vision (camera) sensors, etc. Hybridization of neural network and nondeterministic algorithm. Algabri M, Mathkour H, Ramdane H, et al. We do not focus on any special task but rather want to verify whether conditioning models can be used for adaptively solving tasks of a physical robot at all. 09:30. This ACO algorithm searches the optimal value from the fuzzy rule table and minimizes the distance between the start points to goal point of the mobile robot with obstacle avoidance competence. In Li,72 the authors have constructed behaviour-based neuro-fuzzy control architecture for a mobile robot navigation in an unstructured environment. Nakamura E, Kehtarnavaz N. Optimization of Fuzzy Membership Function Parameters. 0000294711 00000 n Wow. Al-Jarrah et al.62 have described the path planning and coordination of multiple mobile robots using probabilistic neuro-fuzzy architecture. IEEE International Conference on Mechatronics and Automation. Zhang et al.121 have proposed the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO) to search a collision-free optimal path in the uncertain dynamic environment. I liked the fact that Literature Review On Obstacle Avoidance Robot the paper was delivered a couple of hours before my deadline. The authors have presented many simulation tests using Khepera Simulator (KiKs). Metropolis N, Rosenbluth AW, Rosenbluth MN, et al. According to literature survey, most of the researchers have used these soft computing techniques for mobile robot navigation and obstacle avoidance in only static environments. A Comparative Study on Some Navigation Schemes of a Real Robot Tackling Moving Obstacles. 0000298698 00000 n Hsu & Juang147 have adopted the multi-objective ACO for optimized the rule parameters of the fuzzy controller (FC) for wall-following mobile robot. Synodinos & Aspragathos118 have integrated simulated annealing algorithm and artificial potential field method to rescue the robot from undesired local minima problem during navigation. Kumar D, Dhama Neuro–Fuzzy Control of an Intelligent Mobile Robot. Mohanta et al.93 have designed Petri-GA technique to optimize the navigation path length of multiple mobile robots in the cluttered environment. 0000300387 00000 n Keywords: mobile robot, sensor, actuator, navigation, obstacle avoidance. 0000311009 00000 n ROBOT has sufficient intelligence to cover the maximum area of provided space. In Kin,65 the authors have proposed type-2 fuzzy neural network (IT2FNN) to solve the obstacle avoidance and position stabilization problems of wheeled mobile robots. Zhang et al.116 have combined the simulated annealing algorithm and Ant Colony Optimization (ACO) algorithm to increase the navigation speed of the mobile robot. 0000010087 00000 n Al–Jarrah R, Shahzad A, Roth H. Path Planning and Motion Coordination for Multi–Robots System Using Probabilistic Neuro–Fuzzy. In: IEEE International Congress of Engineering Mechatronic and Automation (CIIMA), 2013. p. 1–6. Behavior–Based Neuro–Fuzzy Controller for Mobile Robot Navigation. 0000012484 00000 n Genetic algorithm for mobile robot navigation. Dynamic Robot Path Planning using an Enhanced Simulated Annealing Approach. Zhao Y, Zu W. Real–Time Obstacle Avoidance Method for Mobile Robots Based on a Modified Particle Swarm Optimization. Mobile robot navigation and obstacle avoidance techniques a review. In Luo,153 the authors have presented a review paper of multi-sensor fusion and integration and its application in the field of Mechatronic. Kim CJ, Chwa D. Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type–2 Fuzzy Neural Network. DOI: 10.15406/iratj.2017.02.00023. 2015;13(4):913–918. Toggle navigation. Where, the fuzzy logic is used to handle the uncertainty of the environment, and the neural network is used to tune the parameters of membership functions. Open Access ABSTRACT. Kubota N, Morioka T, Kojima F, Fukuda T. Learning of Mobile Robots Using Perception–Based Genetic Algorithm. Li Q, Tang Y, Wang L, et al. A Knowledge based Genetic Algorithm for Path Planning in Unstructured Mobile Robot Environments. 536–541. Roadmap–Based Path Planning–Using the Voronoi Diagram for a Clearance–Based Shortest Path. 0000294927 00000 n Hui NB, Mahendar V, Pratihar DK. They have used PSO algorithm to determine the optimal input/output membership function parameters and rules for the fuzzy type-2 controller. 0000027355 00000 n They have used gradient descent learning algorithm to adjust the membership function parameters of the ANFIS. Both the controllers receive inputs from the different sensors to avoid the obstacles when the robot moves towards the desired goal. Liang et al.150 have developed a bacterial foraging algorithm for making a bio-inspired path planning strategy for a mobile robot. Elshamli A, Abdullah HA, Areibi S. Genetic Algorithm for Dynamic Path Planning. Li et al. This neural network technique is motivated from the human brain, which is being applied by many researchers in the different fields such as signal and image processing, pattern recognition, mobile robot path planning, and business, etc. Comparative Study of Geometric Path Planning Methods for a Mobile Robot: Potential Field and Voronoi Diagrams. How to Create an Literature Review On Obstacle Avoidance Robot Intro for Any Writing Piece; Specialized on: English; History; Sociology; Nursing; Education; DrIanWan offline. Brand & Yu151 have applied the Firefly Algorithm (Glow-worm swarm optimization) to find a collision free shortest path in the two-dimensional static and dynamic environment for a mobile robot. Liu Q, Lu YG, Xie C Optimal Genetic Fuzzy Obstacle Avoidance Controller of Autonomous Mobile Robot Based on Ultrasonic Sensors. To prepare an optimal intelligent controller for an autonomous wheeled mobile robot, the Castillo et al.120 have designed the hybridization of an Ant Colony Optimization (ACO) algorithm and the Particle Swarm Optimization (PSO) algorithm to optimize the membership function of a fuzzy controller. Godjevac J, Steele N. Neuro–Fuzzy Control of a Mobile Robot. Baturone I, Gersnoviez A, Barriga A. Neuro–Fuzzy Techniques to Optimize an FPGA Embedded Controller for Robot Navigation. By Kim Dorsey | January 26, 2021. 3735 165 Our tutors belong to some prestigious institutions of the world which include: Learn More Frequently Asked Questions Customer Success Stories Career Advice Blog Resume Samples NEED HELP? Castillo et al.102 have designed Multiple Objective Genetic Algorithm (MOGA) for navigation path optimization of the mobile robot. Zhang Y, Gong DW, Zhang JH. 0000121972 00000 n A Sensor–Based Navigation for a Mobile Robot using Fuzzy Logic and Reinforcement Learning. A Genetic–Fuzzy Approach for Mobile Robot Navigation among Moving Obstacles. Hsu CH, Juang CF. Yanar TA, Akyurek Z. Both the controllers receive inputs (obstacle distance) from the left and right ultrasonic sensors to control the left and right velocities of the motors of the mobile robot. Ren et al.26 have designed an intelligent fuzzy logic controller to solve the navigation problem of wheeled mobile robot in an unknown and changing environment. TheCaptain offline. The Self-configurable and Transformable Omni-Directional Robotic Modules (STORM) is a novel approach towards â¦ 0000319742 00000 n Neuro–Fuzzy Approach to Obstacle Avoidance of a Nonholonomic Mobile Robot. In this paper an Obstacle Avoiding Robot is â¦ Intelligent Autonomous Vehicle Navigated by Using Artificial Neural Network. Sariff & Buniyamin141 have compared the performances of GA and ACO algorithm for robot path planning in the global static environment and stated that the ACO algorithm takes less time to search an optimal path in the environment compared to GA. Hsu et al.142 have proposed an improved ant colony system algorithm by including a new pheromone updating parameter for path planning of mobile robots. 2.4âGHz transmitter for transmitting video is used by the operator/user to direct the â¦ Fuzzy reinforcement learning sensor-based mobile robot navigation has been presented by Beom & Cho35 for complex environments. To evaluate literature related to the fields of engineering, mechatronics, and software development in the design, construction, and programming of an autonomous robot. Chen et al.148 have designed a scent pervasion (pheromone) principle of ant (ACO) based robotic path planning in a map environment. Hence, obstacle avoidance is activated, and the robot circumvents the obstacle by turning and attempting to stay outside the safe radius R s = 1 m. According to the theory described in section 2.2, the USR converges to a constant offset of the safe radius, which could be made smaller by a different choice of control gains. 0000318590 00000 n IEEE Latin America Transactions. In the proposed model, the behavior of bacteria is applied to search an optimal collision-free path between the start nodes to the target node in an environment with obstacles. 0000008474 00000 n Propose a strategy for coordinated control with obstacle avoidance for the two robot manipulators in the lab 3. Chohra A, Farah A, Benmehrez C. Neural Navigation Approach for Intelligent Autonomous Vehicles (IAV) in Partially Structured Environments. Mobile Robot Path Planning based on Adaptive Bacterial Foraging Algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Deterministic, Nondeterministic, and Evolutionary algorithms, etc. Thank you so much! El–Teleity SAL, Nossair ZB, et al. Equation of State Calculations by Fast Computing Machines. The authors have designed two behavior control actions for navigation, namely obstacle avoidance behavior and the goal-seeking behavior. Group, All rights reserved. Mohajer B, Kiani K, Samiei E, et al. So this system provides an alternate way to the existing system by â¦ 0000246071 00000 n In Ganapathy,139 the authors have described various behaviours such as goal-seeking, wall-following obstacle avoidance for mobile robot navigation using improved ACO algorithm. Marichal GN, Acosta L, Moreno L, et al. 0000298911 00000 n In Lee,83 the authors have developed a Takagi-Sugeno type recurrent neuro fuzzy system and hybrid algorithm (genetic algorithm with particle swarm optimization) to improve the path tracking stability of the mobile robots. Moreover, the authors have tested their developed method in various simulation environments and compared it with traditional GA techniques and stated that their developed mutation operator based GA performs better over traditional GA. In Janglova,63 the author has presented a neural network-based technique for intelligent path planning and control of a mobile robot. 3.1 Introduction 13 3.2 Flowchart of project progression 14 3.3 Situation when encounter an Obstacle 15 3.4 Introduction in designing the Algorithm 16 3.5 Software Implementation 17 3.5.1 Simulator using Sim.i.Am â¦ 0000298486 00000 n Zhu Q, Yan Y, Xing Z. Singh MK, Parhi DR. Joshi MM, Zaveri M. Neuro–Fuzzy Based Autonomous Mobile Robot Navigation System. To hire a tutor you need to send Obstacle Avoiding Robot Literature Review in your request through the form given below. 0000013326 00000 n Implement a simulator for the robot manipulator 4. Robot Navigation in Very Cluttered Environments by Preference–Based Fuzzy Behaviors. Guan–Zheng TAN, Huan H, Sloman A. Ant Colony System Algorithm for Real–Time Globally Optimal Path Planning of Mobile Robots. In this work, they have designed four ANFIS controllers to control the left and right angular velocities, and angle between the robot and target (heading angle). 0000037051 00000 n +1-918-917-5848, Copyright © 2014-2021 MedCrave Raja P, Pugazhenthi S. Path Planning for Mobile Robots in Dynamic Environments using Particle Swarm Optimization. In this study, the genetic algorithm is employed to adjust the fuzzy membership function and weight of the neural network. Y. V. Chavan ORCID: orcid.org ... âStereo vision based obstacle avoidance in indoor environmentâ. Famous Persons; Geographical Information; Srikakulam Population; Mandals in Srikakulam district; About the City 0000296818 00000 n Li THS, Chang SJ, Tong W. Fuzzy Target Tracking Control of Autonomous Mobile Robots by Using Infrared Sensors. Nichols E, McDaid LJ, Siddique N. Biologically Inspired SNN for Robot Control. The proposed controller receives input (obstacle distance) from the array of sensors to actuate the left and right wheel velocities of the mobile robots. 0000424301 00000 n Masehian & Sedighizadeh124 have solved the motion planning problem of the mobile robot by using multi-objective PSO. Zhao & Zu119 have developed a Modified Particle Swarm Optimization (MPSO) technique for mobile robot navigation in the dynamic environment. The authors have used eight ultrasonic range finder sensors for surrounding obstacle detection as the input of the neuro-fuzzy controller for selecting the correct left and the right wheel speeds for a mobile robot. Al Mutib & Mattar70 have proposed the sensor-based navigation of mobile robot using neuro-fuzzy architecture. 0000298167 00000 n Purian & Sadeghian136 have explored the optimal path for a mobile robot in an unknown dynamic environment using Ant Colony Optimization (ACO) algorithm and fuzzy controller. Széchenyi Motlagh et al.58 have presented the target seeking, and obstacle avoidance behaviours using neural networks and reinforcement learning. 0000008030 00000 n Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. Rusu CG, Birou IT, Szoke E. Fuzzy Based Obstacle Avoidance System for Autonomous Mobile Robot. Takahashi O, Schilling RJ. Bhattacharya P, Gavrilova ML. Lee CH, Chiu MH. work at https://medcraveonline.com This algorithm searches the feasible path in the environment by randomly in every iteration. 0000014210 00000 n The advantages of the improved genetic algorithm are capable of guiding the mobile robots efficiently from the starting node to end node without any collision in the environment. Brahmi H, Ammar B, Alimi AM. Automatic Navigation of Mobile Robots in Unknown Environments. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The authors have compared the performance of different membership functions such as triangular, trapezoidal and gaussian for mobile robot navigation and stated that the gaussian membership function is more efficient for navigation. for mobile robot navigation and tested it in various simulation environments. Forty-eight Fuzzy rules and two behaviours, target seeking, and obstacle avoidance are designed using this model. Multi–Objective Continuous–Ant–Colony–Optimized FC for Robot Wall–Following Control. Recurrent Neuro Fuzzy Control Design for Tracking of Mobile Robots via Hybrid Algorithm. Tuesday, March 2, 2021 2 PM ET / â¦ In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. Ganapathy V, Jie TTJ, Parasuraman S. Improved Ant Colony Optimization for Robot Navigation. I really like the job you do. From the literature survey, it is observed that many researchers have demonstrated only computer simulation results without implementations of physical robot. Brand M, Yu XH.Autonomous Robot Path Optimization using Firefly Algorithm. Bi et al.137 have designed an Ant Colony System (ACS) to improve the path searching speed of the mobile robot in the dynamic environment. 2.0 Introduction 4 2.1 Basic Concept for Obstacle Avoidance 5 2.2 Reviews of previous related works 6 2.3 Summary of reviews 11 3 METHODOLOGY 13 . 505 completed orders. Xiao H, Liao L, Zhou F. Mobile Robot Path Planning Based on Q–ANN. 422–427. Neural Network Dynamics for Path Planning and Obstacle Avoidance. 0000298592 00000 n Montaner MB, Ramirez–Serrano A. This objective function works based on the position of the obstacles and target in the environment. %PDF-1.4 %�� Zou et al.52 have presented the literature survey of neural networks and its applications in mobile robotics. Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments. Liang XD, Li LY, Wu JG, et al. 0000013938 00000 n Qing–yong B, Shun–ming L, Wei–yan S, et al. Al–Araji AS, Abbod MF, Al–Raweshidy HS. Algabri et al.39 have combined the fuzzy logic with other soft computing techniques such as Genetic Algorithm (GA), Neural Networks (NN), and Particle Swarm Optimization (PSO) to optimize the membership function parameters of the fuzzy controller for improving the navigation performance of mobile robot. Client #2541144. theadager. 0000112907 00000 n Pothal & Parhi86 have proposed a sensor based adaptive neuro-fuzzy inference controller for navigation of single and multiple mobile robots in the highly cluttered environment. Where the fuzzy controller provides the initial membership function and the genetic algorithm choose the best membership value to optimize the fuzzy controller for mobile robot navigation. Rai N, Rai B. Neural Network based Closed loop Speed Control of DC Motor using Arduino Uno. 0000058210 00000 n Chatterjee A, Matsuno F. A Geese PSO Tuned Fuzzy Supervisor for EKF based Solutions of Simultaneous Localization and Mapping (SLAM) Problems in Mobile Robots. 2017;2(3):96-105. 0000015687 00000 n Time–Optimal, and Collision–Free Navigation of a Car–Like Mobile Robot using Neuro–Fuzzy Approaches. 0000014323 00000 n Das T, Kar IN, Chaudhury S. Simple Neuron–Based Adaptive Controller for a Nonholonomic Mobile Robot Including Actuator Dynamics. Brahmi et al.68 have solved the path planning and localization problem of mobile robot using recurrent neural network (RNN). 0000297032 00000 n Shiltagh & Jalal129 have investigated the application of Modified Particle Swarm Optimization (MPSO) in the field of mobile robotics to determine a shortest feasible path from the beginning to end in an environment between obstacles. 0000296604 00000 n user1227538. Adaptive Fuzzy Control for Trajectory Tracking of Mobile Robot. An obstacle avoiding robot is an intelligent device, which can automatically sense and overcome obstacles on its path. The neural network is used to train the robot to reach the goal, and fuzzy architecture is integrated with it to control the velocities of the robot. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Miao & Tian115 have presented a simulated annealing algorithm based intelligent navigational controller, which helps the robot to search an optimal or near-optimal path in the static and dynamic environments. Fan et al.140 have applied an intensified ant colony optimization (ACO) algorithm to search an optimal path for mobile robot between irregular obstacles in an environment. 0000238297 00000 n 0000236856 00000 n Regret for the inconvenience: we are taking measures to prevent fraudulent form submissions by extractors and page crawlers. Following are the two sensors which are used for this purpose: PSO-based optimal fuzzy controller has been designed by Wong et al.125 to determine the velocities of the left-wheeled motor and right-wheeled motor of the differential drive mobile robot. 2011. A Fast Two–Stage ACO Algorithm for Robotic Path Planning. The two neural network controllers are applied to path planning and control. 0000299445 00000 n Int Rob Auto J 2(3): 00022. GA, genetic algorithm; NN, neural networks; PSO, particle swarm optimization; PWM, pulse width modulation; RNW-PSO, random inertia weight particle swarm optimization; ANN, artificial neural network; FL, fuzzy logic; Gas, Genetic Algorithms; MOGA, multiple objective genetic algorithm; SAA, simulated annealing algorithm; ACO, ant colony optimization; MPSO, modified particle swarm optimization; PSO, particle swarm optimization; RAOFC, reinforcement ant optimized fuzzy controller.