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Optimization on Initial Position and Number of Informed Agents in a Flocking in Presence of Obstacles

Paygani, Alireza | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41073 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Haeri, Mohammad
  7. Abstract:
  8. dFlocking in multi-agent systems as one of the most interested phenomena has gained scientists and researchers attention in recent years. In a multi-agent system, agents by using a consensus algorithm, which designed based on collective, collision avoidance, and velocity adaption rules, try to catch a common goal together. The aims which may define for flocking of a multi-agent system include the getting a certain point, chasing an alien agent, moving in a given route, and so on. Without considering flock’s purposes, all agents must be aware about the goal to catch it. But it is not possible always to inform all agents about the goal. Because sending the goal information to all agents due to data transmission to many points and outfit all agents with communication systems enforces high expenses on system. Also in some applications like military cases informing all agents endangers system’s security. So to avoid these problems the agents of the flock divided in two parts, informed agents which have the information of the goal and uninformed agents which are not aware about the goal. Now we have a new issue, how to select informed agents number and position in order to maximize convergence of the uninformed ones. In this thesis three methods are proposed to determine the number and initial position of informed agents. By implementing large number of simulations, these methods are studied in the number of informed agents, convergence percentage of uninformed agents, quasi-flock forming time, and convergence time. Moreover the performance of these methods is checked in free and constrained spaces
  9. Keywords:
  10. Multiagent System ; Flocking ; Informed and Uninformed Agents ; Informed Agents Selection ; Convergence to Goal ; Obstacle Presence

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