Authors:
Ryo Takahashi
1
;
Munehiro Takimoto
1
and
Yasushi Kambayashi
2
Affiliations:
1
Tokyo University of Science, Japan
;
2
Nippon Institute of Technology, Japan
Keyword(s):
Mobile Agent, Multiple Robots, Ant Colony Optimization, Swarm Intelligence.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Mobile Agents
;
Multi-Agent Systems
;
Operational Research
;
Simulation
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper presents an algorithm for cooperatively transporting objects by multiple robots without any initial knowledge. The robots are connected by communication networks, and the controlling algorithm is based on the pheromone communication of social insects such as ants. Unlike traditional pheromone based cooperative transportation, we have implemented the pheromone as mobile software agents that control the mobile robots corresponding to the ants. The pheromone agent has the vector value pointing to its birth location inside, which is used to guide a robot to the birth location. Since the pheromone agent can diffuse with migrations between robots as well as a physical pheromone, it can attract other robots scattering in a work field to the birth location. Once the robot finds an object, it briefly pushes the object, measuring the degree of the inclination of the object. The robot generates a pheromone agent with the vector value to pusing point suitable for suppressing the incli
nation of the object. The process of the pushes and generations of pheromone agents enables the efficient transportation of the object. We have implemented a simulator based on our algorithm, and conducted experiments to demonstrate the feasibility of our approach.
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