Mohamed Abdelwhab
Papers
1
Total Citations
3
H-Index
1
About
Mohamed Abdelwhab is a researcher focused on intelligent robotics and autonomous navigation, with a particular emphasis on obstacle avoidance and path planning for mobile robots. His most cited work, "Tackling Dead End Scenarios by Improving Follow Gap Method with Genetic Programming" (2018), addresses a critical challenge in robotics: escaping local minima and dead-end situations during navigation. By integrating the Follow Gap Method (FGM) with Genetic Programming (GP), Abdelwhab developed a two-stage controller that enables robots like the Robotino—equipped with nine infrared sensors—to dynamically adapt and find viable paths in complex environments. This innovative approach enhances the robustness of autonomous systems, reducing the risk of robots becoming stuck in cluttered spaces. With 3 citations, this paper has laid groundwork for further research in evolutionary robotics and sensor-based control. Abdelwhab’s contributions are valuable for students and researchers in mobile robotics, artificial intelligence, and control systems, offering practical solutions for real-world navigation challenges. His work exemplifies how combining classical algorithms with evolutionary computation can overcome persistent obstacles in autonomous vehicle design.
Research Focus
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Top Papers
- 1