TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

Aouf, Awatef; Boussaid, Lotfi; Sakly, Anis

doi:10.1155/2018/3145436

Cited by 31 publications

(29 citation statements)

References 37 publications

(34 reference statements)

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“…Realizing the challenge of training and updating parameters of ANFIS in path planning which usually leads to local minimum problem, teaching-learning-based optimization (TLBO) was combined with ANFIS to present a path planning method in [269]. The aim was to exploit the benefits of the two methods to obtain the shortest path and time to goal in strange environment.…”

Section: Neuro-fuzzy Path Planningmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

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Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.

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Section: Neuro-fuzzy Path Planningmentioning

confidence: 99%

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Ayawli

Chellali

Appiah

et al. 2018

Journal of Advanced Transportation

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“…These intensity weight Table IV. Comparison of path length traced by Zhao et al 43 and Aouf et al 44 with path obtained by neural network integrated modified DAYANI approach by considering 1 cm = 1 unit.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 11(a) and (b) shows the comparison of the path obtained by Zhao et al 43 using an optimized fuzzy controller through genetic algorithm method and our proposed modified Neural DAYANI method. Similarly, the path comparison of the proposed technique with TLBO-based ANFIS technique as obtained by Aouf et al 44 is represented in Figure 12(a) and (b). The path lengths generated from both the existing techniques and the proposed technique are calculated and presented for comparison in Table IV.…”

Section: Comparison With Existing Navigational Controllersmentioning

confidence: 93%

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Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Chhotray¹,

Parhi²

2019

Robotica

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SummaryThe present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.

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“…Sahoo et al applied neural network method in ROBONOVA humanoid robot for navigation in the cluttered environment having more obstacles and obtained the optimal motion planning of humanoid . Aouf et al developed a novel evolutionary technique using teaching learning‐based optimization algorithm and artificial neuro‐fuzzy interference system for navigation of mobile robots in strange environments with optimal trajectory and minimum traversal time to reach the goal . Singh and Thongam used MATLAB R2014a Mamdani fuzzy system and presented the navigation of mobile robot in static environment.…”

Section: Introductionmentioning

confidence: 99%

Implementation of grey wolf optimization controller for multiple humanoid navigation

Muni

Parhi

Kumar

2020

Computer Animation & Virtual

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In this paper, grey wolf optimization controller (GWOC) is considered as a multiobjective technique for multiple humanoid navigations. Upon activation of GWOC, the humanoids mimic the group hunting behavior of grey wolves and navigate toward the target in a collision-free manner in presence of both static and dynamic hurdles. The wolves in the pack will either diverge for searching prey or converge together for attacking the prey following the best search agent (Leader Alpha). GWOC has the ability to keep the humanoid free from being trapped in local minima whereas it facilitates it to head toward global minima. GWOC provides better results as compared to other intelligent techniques because of its five characteristics that include safe boundary, protection, following, hunting, and caring. Both simulation and experimental navigation in laboratory conditions for single as well as for multiple humanoid NAOs have been carried out. From the results of simulation and experimental data, it is confirmed that GWOC provides global minima for humanoid robots in complex environments with different shaped obstacles. A Petri-net controller is considered while navigating multiple humanoids, as during multiple humanoid navigations, one humanoid robot acts as a dynamic obstacle to other humanoids. K E Y W O R D SGWOC, NAO humanoid, navigation, petri-net model INTRODUCTIONLot of research works have been performed for path navigation of mobile robots, biped robots, and industrial robots by using both classical and artificial intelligent techniques. But there is still some improvement needed to avoid the trapping of robots in local minima. Today, the main challenging task for researchers is the path navigation of humanoids having multidegrees of freedom by using computational methods. Though several researchers are working on it, still it is in virtue to obtain the best intelligent method for effective and efficient navigation. For this purpose, grey wolf optimization controller (GWOC) is considered. The humanoid NAO considered for this research work is developed by ALDEBARAN robotics France, which is having 25 degrees of freedom. 1 There are sensors for detecting the obstacles, cameras to take the images of hurdles, motors, actuators, and batteries to perform the predefined tasks. The obstacle distances and shape of the obstacles are detected by inbuilt ultrasonic sensors and camera mount on the head. The programmed GWOC activates when NAO finds any obstacle in its path while heading toward target. Several researchers have worked toward the navigation of different kinds of robots using many intelligent techniques as path navigation of robots plays a Comp Anim Virtual Worlds. 2020;31:e1919. wileyonlinelibrary.com/journal/cav

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TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

Cited by 31 publications

References 37 publications

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

Implementation of grey wolf optimization controller for multiple humanoid navigation

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