Mobile robot path planning using an improved ant colony optimization
Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematu...
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| Veröffentlicht in: | International journal of advanced robotic systems Jg. 15; H. 3 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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London, England
SAGE Publications
01.05.2018
Sage Publications Ltd SAGE Publishing |
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| Abstract | Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms. |
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| AbstractList | Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms. |
| Author | Akka, Khaled Khaber, Farid |
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| Keywords | Ant colony optimization mobile robot path planning grid map optimal path |
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| SubjectTerms | Algorithms Ant colony optimization Computer networks Computer simulation Convergence Distributed processing Evaporation rate Path planning Robots Visibility |
| Title | Mobile robot path planning using an improved ant colony optimization |
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