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 effective and effcient 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.
Computer Science & Engineering Department
MS in Computer Science
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M. Youssef, A.
(2018).Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions [Master’s thesis, the American University in Cairo]. AUC Knowledge Fountain.
M. Youssef, Ahmed. Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions. 2018. American University in Cairo, Master's thesis. AUC Knowledge Fountain.