A genetic-fuzzy approach for mobile robot navigation among moving obstacles
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TLDR
The results of this study show that the proposed genetic-fuzzy approach can produce efficient knowledge base of an FLC for controlling the motion of a robot among moving obstacles.Citations
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Journal ArticleDOI
Reinforcement based mobile robot navigation in dynamic environment
TL;DR: The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.
Journal ArticleDOI
Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review
TL;DR: 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.
Journal ArticleDOI
Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Mobile-Robot Navigation in Unknown Environments
Chia-Feng Juang,Yu-Cheng Chang +1 more
TL;DR: An evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design that dynamically forms different groups to select parents in crossover operations, particle updates, and replacements to improve fuzzy-control accuracy and design efficiency is proposed.
Journal ArticleDOI
A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation
TL;DR: A layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment and is implemented on a real mobile robot, Koala, and tested in various environments.
Journal ArticleDOI
A comprehensive study for robot navigation techniques
TL;DR: An effort has been made to study several navigation techniques, which are well suited for the static and dynamic environment and can be implemented for real-time navigation of mobile robot.
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.