Design and Implementation of Swam Robotics using Flood Fill Algorithm

<p>Swam Intelligence provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. This paper presents design and implementation of swam robotics in a multi-agent environment. At the beginning, robot agents are ignorant of the maze. The robots are programmed with Flood fill algorithm to solve maze. The robot scans maze and stores the values in EEPROM. The robot agent shares the information to other robot agents through wireless communication. The proposed flood fill algorithm is found to be effective tool for solving maze of moderate size.</p>

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Problem Decomposition and Multi-agent System Creation for Distributed Problem Solving

Lecture Notes in Computer Science - Foundations of Intelligent Systems ◽

10.1007/3-540-39963-1_25 ◽

2000 ◽

pp. 237-246

Author(s):

Katsuaki Tanaka ◽

Michiko Higashiyama ◽

Setsuo Ohsuga

Keyword(s):

Problem Solving ◽

Problem Decomposition ◽

Multi Agent System ◽

Agent System ◽

Distributed Problem Solving ◽

Multi Agent

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Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems

Advances in Evolutionary Computing for System Design - Studies in Computational Intelligence ◽

10.1007/978-3-540-72377-6_9 ◽

2007 ◽

pp. 211-227 ◽

Cited By ~ 2

Author(s):

Dipti Srinivasan ◽

Min Chee Choy

Keyword(s):

Problem Solving ◽

Evolutionary Learning ◽

Multi Agent Systems ◽

Agent Systems ◽

Distributed Problem Solving ◽

Multi Agent

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A new spring net approach to distributed problem solving in multi-agent systems

Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004). ◽

10.1109/iat.2004.1342985 ◽

2004 ◽

Author(s):

Xiang Feng ◽

Dianxun Shuai

Keyword(s):

Problem Solving ◽

Multi Agent Systems ◽

Agent Systems ◽

Distributed Problem Solving ◽

Multi Agent

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Multi-agent collaborative service and distributed problem solving

Cognitive Systems Research ◽

10.1016/j.cogsys.2004.03.006 ◽

2004 ◽

Vol 5 (3) ◽

pp. 191-206 ◽

Cited By ~ 12

Author(s):

Jiming Liu ◽

Xiaolong Jin ◽

Yi Tang

Keyword(s):

Problem Solving ◽

Distributed Problem Solving ◽

Multi Agent

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Multi-Cellular Techniques

Handbook of Research on Nature-Inspired Computing for Economics and Management ◽

10.4018/978-1-59140-984-7.ch002 ◽

2007 ◽

pp. 16-27

Author(s):

C. Anderson

Keyword(s):

Problem Solving ◽

Task Allocation ◽

Lessons Learned ◽

Collective Phenomena ◽

Distributed Problem Solving ◽

Insect Societies ◽

Nature Inspired Computing ◽

History Of ◽

Multi Agent ◽

Problem Solving Task

Social insects—ants, bees, wasps, and termites—and the distributed problem-solving, multi-agent paradigm that they represent, have been enormously influential in nature-inspired computing. Insect societies have been a source of inspiration and amazement for centuries, but only in the last 25 years or so have we made significant inroads to both understanding just how various collective phenomena arise and are governed, and how we can use the lessons and insights garnered from sociobiological research for more practical purposes. In this chapter, we provide a very brief history of the field, detailing some of the key phenomena, mechanisms, and lessons learned, and a quick tour of some of the different types of applications to which this knowledge has been put to use, including but certainly not limited to distributed problem solving, task allocation, search, and collective robotics.

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