Ali Ghanbari Sorkhi
Papers
2
Total Citations
27
H-Index
2
About
Ali Ghanbari Sorkhi is a researcher advancing the frontiers of autonomous systems and robotics, with a core focus on path planning, optimization algorithms, and motor control. His most influential work introduces a novel reinforcement learning-based multi-operator differential evolution algorithm, enhanced with cubic spline interpolation, to tackle the critical path planning problem in autonomous driving. This approach, published in 2025, has already garnered 19 citations, reflecting its immediate impact on generating safer and more efficient routes by overcoming the limitations of traditional graph-based methods. In parallel, Sorkhi has made significant strides in robotics control, developing a hybrid Moth-flame Particle Swarm Optimization (MFPSO) algorithm for precise motor speed control in four-wheel differential drive robots. This work, with 8 citations, addresses the challenging PID controller tuning problem, enabling superior maneuverability in automated vehicles. His contributions lie at the intersection of reinforcement learning, metaheuristic optimization, and practical robotics, offering scalable solutions to real-world autonomous navigation challenges. Sorkhi’s research is particularly notable for its innovative fusion of machine learning with evolutionary computation, setting a new benchmark for robust, adaptive control in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 2