Mobile robot path planning and tracking using simulated annealing and fuzzy logic control

Martínez-Alfaro, H.; Gómez-Garcı́a, Simón

doi:10.1016/s0957-4174(98)00055-4

Cited by 86 publications

(39 citation statements)

References 5 publications

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“…Sensor-based autonomous navigation of a mobile robot in the dynamic environment has been presented by Chang & Song [106]. Martinez-Alfaro et al [107] have developed the simulated annealing and fuzzy logic for designing an automatic path planning technique for mobile robot. The simulated annealing algorithm is used to search a collisionfree optimal trajectory between the fixed polygonal obstacles, and forty-nine fuzzy rules are applied to adjust the velocity of the robot during navigation.…”

Section: Simulated Annealing Algorithm For Mobile Robot Navigationmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

176

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. 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 mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. 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. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. 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. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Section: Simulated Annealing Algorithm For Mobile Robot Navigationmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

176

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“…A comprehensive review illustrating the application of various non-linear optimization schemes to the optimization of FLC can be found in Pratihar and Hui (2007). Some of the representative algorithms used by researchers are Least square method (Song & Smith, 2000), Genetic-Fuzzy approach (Cardenas, Aguilar, & Rodriguez, 2009;MartÃ-nez, Castillo, & Aguilar, 2009) Gradient descent approach (Rajasekaran & Vijayalakshami, 2003), Ant Colony optimization (Juang, Lu, Lo, & Wang, 2008), Reinforcement Learning (Berenji, 1992), Tabu Search (Pothiya, Ngamroo, & Kongprawechnon, 2007), Taguchi Method (Chien et al, 2008), and Simulated Annealing (Martinez-Alfaro & Gomez-Garcia, 1998;Han, Jeong, & Park, 2005) optimization algorithm. Though all these algorithms have been successfully implemented in a variety of applications, gradient descent approach and GA-Fuzzy method are more frequently used by researchers since they provide better accuracies.…”

Section: Optimization Of Fuzzy Logic Controllermentioning

confidence: 99%

An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Xie

Jamwal

2011

Expert Systems with Applications

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“…Martıńez-Alfaro and Gómez-Garcıá applied fuzzy control and simulated annealing (SA) to accomplish the obstacle avoidance for a mobile robot [5]. Genetic algorithm (GA) is used in the papers where the fitness is evaluated with respect to the path length or sum of angles and where genetic operators are conducted for the evolution [6].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Mobile Robots in Dynamic Environment Using Improved Bacterial Foraging Algorithm

Via¹

2018

GEOMATE

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ABSTRACT:The topic of navigation is one of the focused problems in the correlation of autonomous mobile robots. It is necessary to have a methodology to accomplish a collision path of navigation from an initial position (state) to a target position (goal). This paper presents a new method for path planning to determine a feasible, optimal, and safe path. Based on Bacterial Foraging algorithm, we propose an improved algorithm for solving the one-line path planning of mobile robots by choosing the proper objective function for the target, obstacles, and robots collision avoidance. This algorithm finds a path towards the target and avoiding the obstacles using particles, which are randomly distributed, on a determined area around a robot. The criterion on which it selects the best article is the distance to the target. By the proposed approach, the next positions of each robot are designed.

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Mobile robot path planning and tracking using simulated annealing and fuzzy logic control

Cited by 86 publications

References 5 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Path Planning for Mobile Robots in Dynamic Environment Using Improved Bacterial Foraging Algorithm

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