Empowering Mobile Software Agents

2002 ◽  
pp. 47-63 ◽  
Author(s):  
Volker Roth
Keyword(s):  
Software Agents ◽  
Mobile Software

A Multi-Robot System Using Mobile Agents with Ant Colony Clustering

2010 ◽  
pp. 174-192
Author(s):  
Yasushi Kambayashi ◽  
Yasuhiro Tsujimura ◽  
Hidemi Yamachi ◽  
Munehiro Takimoto
Keyword(s):  
Communication Networks ◽  
Mobile Agents ◽  
Software Agents ◽  
Ant Colony ◽  
Multiple Robots ◽  
Robot System ◽  
Mobile Software ◽  
Robot Systems ◽  
Ant Colony Clustering ◽  
Multi Robot

This chapter presents a framework using novel methods for controlling mobile multiple robots directed by mobile agents on a communication networks. Instead of physical movement of multiple robots, mobile software agents migrate from one robot to another so that the robots more efficiently complete their task. In some applications, it is desirable that multiple robots draw themselves together automatically. In order to avoid excessive energy consumption, we employ mobile software agents to locate robots scattered in a field, and cause them to autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO) method. ACO is the swarm-intelligence-based method that exploits artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. Even though there is much room to improve the collaboration of multiple agents and ACO, the current results suggest a promising direction for the design of control mechanisms for multi-robot systems. In this chapter, we focus on the implementation of the controlling mechanism of the multi-robot system using mobile agents.

Mobile Software Agents for Mobile Applications

2012 ◽  
pp. 725-740 ◽  
Author(s):  
Oscar Urra ◽  
Sergio Ilarri ◽  
Raquel Trillo ◽  
Eduardo Mena
Keyword(s):  
Mobile Applications ◽  
Software Agents ◽  
Mobile Software

Mobile Software Agents for Telecommunication Applications

2000 ◽  
Vol 23 (8) ◽  
pp. 705-707 ◽  
Author(s):  
T Magedanz ◽  
A Karmouch
Keyword(s):  
Software Agents ◽  
Mobile Software

scholarly journals Formation Control of Swarm Robots Using Mobile Agents

2019 ◽  
Vol 06 (02) ◽  
pp. 193-222 ◽  
Author(s):  
Yasushi Kambayashi ◽  
Hideaki Yajima ◽  
Tadashi Shyoji ◽  
Ryotaro Oikawa ◽  
Munehiro Takimoto
Keyword(s):  
Control System ◽  
Robot Control ◽  
Formation Control ◽  
Partial Information ◽  
Software Agents ◽  
Control Programs ◽  
Leafcutter Ants ◽  
Mobile Software ◽  
Swarm Robots ◽  
Robot Control System

In this paper, we propose an algorithm for controlling a fleet of swarm robots that construct three-dimensional forms. The swarm robots coordinate with each other through network communication, and compose formations such as polyhedrons presented as spherical coordinates. Our control algorithm achieves communication through mobile software agents, which introduce control programs to robots that initially have no information about the formation. Mobile software agents are autonomous objects that can migrate from one robot to other robots through a communication network and can deliver control programs as they are needed. We have made our swarm robot system to mimic the behaviors of the leafcutter ants. A leafcutter ant is a typical social insect and uses pheromone for communication. In our robot control system, we have implemented ants and pheromones as mobile software agents. We call the mobile software agents that drive the mobile robots as ant agents, and call the other agents that provide communication as pheromone agents. The ant agents drive the swarm robots to locations identified by the pheromone agents. Each ant agent has only partial information. There is no need for either a central control or an agent that has the entire design of the formation. In order to diffuse the partial information among the neighboring robots, each ant agent generates pheromone agents and dispatches them to the surrounding robots. Dispatched pheromone agent looks for a proper ant agent to influence towards a desired relative location. It is the ant agent that actually drives the robot by following the guidance of the pheromone agent, and the collective actions of ant agents and pheromone agents achieve the composition of the objective formation. We have implemented a simulator based on our algorithm and conducted numerical experiments. The results demonstrate that our mobile robot control system is feasible and efficient in practice in practical situations.

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