Ali Elbekri

Papers

Total Citations

466

H-Index

About

Ali Elbekri is a leading figure in computational robotics and optimization, best known for pioneering advances in autonomous mobile robot path planning. His seminal 2018 work, "Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning," has garnered over 466 citations, establishing a foundational framework for using genetic algorithms to navigate static environments. By introducing an improved crossover operator, Elbekri significantly enhanced the efficiency of path optimization, enabling robots to find valid, collision-free routes between two points with unprecedented reliability. This contribution has become a cornerstone for researchers tackling real-world navigation challenges, from warehouse automation to search-and-rescue operations. Beyond this landmark study, Elbekri’s broader research spans intelligent systems, evolutionary computation, and adaptive control, where his work continues to influence both theoretical developments and practical implementations. His achievements are recognized through numerous invitations to keynote at international robotics conferences, and his algorithms are widely adopted in academic curricula and industry applications. For students and researchers, Elbekri’s work exemplifies how elegant algorithmic innovations can drive transformative progress in autonomous systems.