Umar Adeel

University of Sannio, American University of Ras Al Khaimah

Papers

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About

Umar Adeel is a robotics researcher whose work bridges classical control theory and modern deep learning to advance mobile robot autonomy. His research focuses on three core areas: self-balancing systems, intelligent path planning, and adaptive autonomy for mobile robots. Adeel’s early work on an autonomous dual-wheel self-balancing robot (2013, 7 citations) demonstrated how PID control could stabilize an inherently nonlinear, unstable system through careful physical redesign—a foundational contribution to low-cost balancing platforms. More recently, he has pioneered deep learning approaches to navigation, including a CRNN and A*-based path-planning method (2023, 4 citations) that combines convolutional recurrent neural networks with classical search algorithms for efficient, real-time route generation. His latest work (2025, 2 citations) introduces an improved A* algorithm integrated with dynamic programming to enable adaptive autonomy, allowing mobile robots to handle unforeseen scenarios without a one-size-fits-all design criterion. This progression from microcontroller-based control to AI-driven planning reflects Adeel’s commitment to creating robust, sustainable navigation solutions. His research is particularly valuable for students and engineers developing autonomous systems that must operate reliably in unpredictable environments.