Ant Colony Clustering Using Mobile Agents as Ants and Pheromone

This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, an ant convey an object, but in our approach, the ant implemented as a mobile softwar...

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Published inIntelligent Information and Database Systems pp. 435 - 444
Main Authors Mizutani, Masashi, Takimoto, Munehiro, Kambayashi, Yasushi
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
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Abstract This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, an ant convey an object, but in our approach, the ant implemented as a mobile software agent controls the robot corresponding to an objects, so that the object moves to the direction required by the ant agent. At this time, the process where an ant searches an object corresponds to some migrations of the ant agent, which are much more efficient than physically searching. Also, the ACC uses a pheromone for making a cluster grown and stabilized. However, it is difficult to implement such a pheromone as an physical entity, because it can diffuse, mutually intensify its strength, and restrict its effect in its scope. In our approach, the pheromone is implemented as a mobile software agent as well as an ant. The mobile software agents can migrate from one robot to another, so that they can diffuse over robots within their scopes. In addition, since they have their strength as vector values, they can represent mutually intensifying as synthesis of vectors. We have been developing elemental techniques for controlling multiple robots using mobile software agents, and showed effectiveness of applying them to the previous ACC approach which requires a host for centrally controlling robots. The new ACC approach decentralizes it, and makes a robot system free from special devices for checking locations.
AbstractList This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, an ant convey an object, but in our approach, the ant implemented as a mobile software agent controls the robot corresponding to an objects, so that the object moves to the direction required by the ant agent. At this time, the process where an ant searches an object corresponds to some migrations of the ant agent, which are much more efficient than physically searching. Also, the ACC uses a pheromone for making a cluster grown and stabilized. However, it is difficult to implement such a pheromone as an physical entity, because it can diffuse, mutually intensify its strength, and restrict its effect in its scope. In our approach, the pheromone is implemented as a mobile software agent as well as an ant. The mobile software agents can migrate from one robot to another, so that they can diffuse over robots within their scopes. In addition, since they have their strength as vector values, they can represent mutually intensifying as synthesis of vectors. We have been developing elemental techniques for controlling multiple robots using mobile software agents, and showed effectiveness of applying them to the previous ACC approach which requires a host for centrally controlling robots. The new ACC approach decentralizes it, and makes a robot system free from special devices for checking locations.
Author Mizutani, Masashi
Kambayashi, Yasushi
Takimoto, Munehiro
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DOI 10.1007/978-3-642-12145-6_45
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Nguyen, Ngoc Thanh
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Snippet This paper presents a new approach for controlling mobile multiple robots connected by communication networks. The control mechanism is based on a specific Ant...
SourceID springer
SourceType Publisher
StartPage 435
SubjectTerms Ant Colony Clustering
Intelligent robot control
Mobile agent
Title Ant Colony Clustering Using Mobile Agents as Ants and Pheromone
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