Using Genetic Algorithm for a Mobile Robot Path Planning

Ghorbani, Amin; Shiry, Saeed; Nodehi, Ali

doi:10.1109/icfcc.2009.28

Cited by 39 publications

(16 citation statements)

References 2 publications

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“…The objectives and the fitness function are kept the same. The test case is run for all combinations of λ varying in (0, 1) and η in 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) May 14-15, Espinho, Portugal (1,6). Again we attempt to minimize the fitness function which will correspond to the ideal values of λ and η.…”

Section: A Optimization Of Single Parameter λmentioning

confidence: 99%

“…Paths which are at a distance nearly equal to the safety margin should have lower values. (6) where S is a normalizing factor. Since the obstacle potential acts to a maximum distance of 2m, S = 2.…”

Section: A Optimization Of Single Parameter λmentioning

confidence: 99%

“…Chang and Liu [5] presented an algorithm for multi objective parallel genetic algorithm for collision free path planning of a wheeled mobile robot. Also, Ghorbani et al [6] proposed optimal trajectories for mobile robots using genetic algorithm. Velagic et al [7] combined the APF method with an obstacle pruning method that used the concept of visibility field to solve the problem of Goals Not Reachable due to Obstacles Nearby (GNRON).…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Non-Dominated Sorting Genetic Algorithm for smooth path planning in unknown environments

Shehata

Schlattmann

2014

2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Autonomous robots have been the focus of attention of most researchers, particularly when it is imputed with terms like intelligence and autonomy. The most important challenge encounters autonomous navigation of a mobile robot is established from large amounts of uncertainties that are coupled with natural environment. This includes hazy and cloudy information of the environment. Moreover, continuous and fast changes of the real environment require a fast response from the robot. Many algorithms have been proposed and amongst these, the potential field algorithm is widely used. This work aims at optimizing some parameters involved in the potential field by the use of Non-Dominated Sorting Genetic Algorithm II (NSGA II). This paper takes into account the safety margin around the obstacle along with the size of the robot which also affects its motion during the optimization process in order to ensure the optimal path.

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Section: A Optimization Of Single Parameter λmentioning

confidence: 99%

Section: A Optimization Of Single Parameter λmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Non-Dominated Sorting Genetic Algorithm for smooth path planning in unknown environments

Shehata

Schlattmann

2014

2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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“…Ghorbani et al [13] have solved the global path planning problem of a mobile robot in the complex environment using genetic algorithm approach. Elshamli et al [92] have presented a genetic algorithm technique for solving the path planning problem of a mobile robot in static and dynamic environments.…”

Section: Genetic Algorithm For Mobile Robot Navigationmentioning

confidence: 99%

“…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. Fuzzy logic [10], Neural network [11], Neuro-fuzzy [12], Genetic algorithm [13], Particle swarm optimization algorithm [14], Ant colony optimization algorithm [15], and Simulated annealing algorithm [16], etc. are successfully employed by various researchers to solve the local navigation problem.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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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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