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

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Awatef Aouf ◽  
Lotfi Boussaid ◽  
Anis Sakly
Keyword(s):  
Mobile Robot ◽  
Mobile Robot Navigation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Navigation Problem ◽  
Travelling Time ◽  
Teaching Learning Based Optimization ◽  
Efficient Alternative ◽  
Teaching Learning

This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.

A New Real Time Path Planning for Mobile Robot Navigation Using Invasive Weed Optimization Algorithm

2014 ◽  
Author(s):  
Prases K. Mohanty ◽  
Dayal R. Parhi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Fitness Function ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Planning Algorithm ◽  
Path Planner

In this article a new optimal path planner for mobile robot navigation based on invasive weed optimization (IWO) algorithm has been addressed. This ecologically inspired algorithm is based on the colonizing property of weeds and distribution. A new fitness function has been formed between robot to goal and obstacles, which satisfied the conditions of both obstacle avoidance and target seeking behavior in robot present in the environment. Depending on the fitness function value of each weed in the colony the robot that avoids obstacles and navigating towards goal. The optimal path is generated with this developed algorithm when the robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed navigational algorithm has been performed through a series of simulation and experimental results. The results obtained from the proposed algorithm has been also compared with other intelligent algorithms (Bacteria foraging algorithm and Genetic algorithm) to show the adaptability of the developed navigational method. Finally, it has been concluded that the proposed path planning algorithm can be effectively implemented in any kind of complex environments.

scholarly journals Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Awatef Aouf ◽  
Lotfi Boussaid ◽  
Anis Sakly
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Mobile Robot Navigation ◽  
Planning Problem ◽  
New Techniques ◽  
Travelling Time ◽  
Optimal Motion Planning ◽  
Efficient Alternative ◽  
Continuous Work

For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment

2020 ◽  
Vol 1 (1) ◽  
pp. 59-67
Author(s):  
P. Pei ◽  
Yu. N. Petrenko
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Industrial Internet Of Things ◽  
Navigation Problem ◽  
Automatic Navigation ◽  
Industrial Internet ◽  
Fuzzy Neural ◽  
Navigation Control

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

scholarly journals Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu ◽  
Kunjie Yu ◽  
Wenli Du
Keyword(s):  
Real World ◽  
Teaching And Learning ◽  
Chemical Engineering ◽  
Search Algorithm ◽  
Population Based ◽  
Metaheuristic Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Real World Problems

Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

scholarly journals Quality Analysis of Multimedia Teaching in Vocal Music Class Combining Elitist Teaching-Learning-Based Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongcai Yang
Keyword(s):  
Optimization Algorithm ◽  
Vocal Music ◽  
Quality Analysis ◽  
Multimedia Teaching ◽  
Quality Of Teaching ◽  
Social And Economic Development ◽  
Teaching Learning Based Optimization ◽  
Music Class ◽  
Teaching Learning

With the social and economic development, there have been more and more abundant multimedia carriers. In this paper, based on the elitist teaching-learning-based optimization algorithm, the factors that affect the quality of teaching are analyzed mainly from the perspective of teachers in terms of the teaching philosophy, the design level of informatization teaching, the application of new teaching models, the evaluation of teaching effects, and other aspects. The quality of multimedia teaching in vocal music class is analyzed, aiming to improve the quality of teaching. The results of this study indicate that the proposed algorithm is effective.

scholarly journals Teaching Learning Based Optimization (TLBO) for energy efficiency in Fog Computing

2021 ◽  
Vol 4 (1) ◽  
pp. 248-252
Author(s):  
Amanpreet Kaur ◽  
Heena Wadhwa ◽  
Pardeep Singh ◽  
Harpreet Kaur Toor
Keyword(s):  
Energy Consumption ◽  
Fog Computing ◽  
High Energy ◽  
Teacher Student ◽  
Huge Data ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Least Energy ◽  
Fog Network

Fog Computing is eminent to ensure quality of service in handling huge volume and variety of data and to display output, or for closed loop process control. It comprises of fog devices to manage huge data transmission but results in high energy consumption, end-to end-delay, latency. In this paper, an energy model for fog computing environment has been proposed and implemented based on teacher student learning model called Teaching Learning Based Optimization (TLBO) to improve the responsiveness of the fog network in terms of energy optimization. The results show the effectiveness of TLBO in choosing the shortest path with least energy consumption.

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