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dc.contributor.advisor El-Ayat, Khaled
dc.contributor.author M. Youssef, Ahmed
dc.date.accessioned 2017-07-18T10:37:23Z
dc.date.available 2017-07-18T22:00:13Z
dc.date.created Summer 2017 en_US
dc.date.issued 2017-07-18
dc.identifier.uri http://dar.aucegypt.edu/handle/10526/5164
dc.description.abstract Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more e ective and e cient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space. en_US
dc.format.extent 96 p. en_US
dc.format.medium theses en_US
dc.language.iso en en_US
dc.rights Author retains all rights with regard to copyright. en
dc.subject Swarm Intelligence en_US
dc.subject Darwinian Particle Swarm Optimization en_US
dc.subject Particle Swarm Optimization en_US
dc.subject Swarm Robotics en_US
dc.subject Argos en_US
dc.subject Search and Rescue Missions en_US
dc.subject.lcsh Thesis (M.S.)--American University in Cairo en_US
dc.title Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions en_US
dc.type Text en_US
dc.subject.discipline Computer Science en_US
dc.rights.access This item is available en_US
dc.contributor.department American University in Cairo. Dept. of Computer Science and Engineering en_US
dc.description.irb American University in Cairo Institutional Review Board approval has been obtained for this item. en_US
dc.contributor.committeeMember El-Kassas, Sherif


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  • Theses and Dissertations [1318]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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