Hassan Bedor

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About

Hassan Bedor is a researcher in swarm robotics, with a focus on developing algorithms that enable groups of simple robots to perform complex tasks through decentralized coordination. His key research areas include obstacle avoidance, adaptive foraging, and evolutionary computation for multi-robot systems. Bedor’s most cited work, “A distributed genetic algorithm for swarm robots obstacle avoidance” (2014, 9 citations), introduces a novel approach that uses a genetic algorithm to train robots to avoid obstacles in varied environments, addressing the critical challenge of preventing damage in swarm deployments. He also authored “Tornado: A Robust Adaptive Foraging Algorithm for Swarm Robots” (2013, 4 citations), which tackles the benchmark foraging problem—inspired by insect swarms—by enabling robots with minimal communication to cooperatively search for and transport items too large for a single unit. These contributions demonstrate Bedor’s commitment to creating scalable, robust solutions for real-world swarm applications, where individual simplicity and collective intelligence are paramount. His work offers valuable insights for students and researchers exploring evolutionary robotics and distributed control.