Sana Charfi

Papers

Total Citations

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About

Sana Charfi is a researcher specializing in mobile robotics and optimization algorithms, with a particular focus on trajectory planning for autonomous systems. Her work explores the comparative effectiveness of evolutionary computation techniques, notably Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), in solving complex path-planning challenges for mobile robots. In her most-cited study, "Which is Better for Mobile Robot Trajectory Optimization: PSO or GA?" (2020), Charfi provides a rigorous analysis of these methods, offering insights into their respective strengths in terms of convergence speed, solution quality, and computational efficiency. This contribution is valuable for researchers and engineers seeking to deploy efficient navigation strategies in dynamic environments. While her citation count is currently modest, her work lays foundational groundwork for further exploration into hybrid optimization approaches. Charfi’s research is particularly relevant to the growing field of autonomous robotics, where reliable and optimized trajectory generation is critical for real-world applications such as warehouse logistics, search-and-rescue operations, and autonomous driving. Her dedication to advancing algorithmic solutions underscores her potential for future impact in robotics and artificial intelligence.