Abdul Moeed Zafar

National University of Sciences and Technology

Papers

Total Citations

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About

Abdul Moeed Zafar is a robotics researcher whose work focuses on whole-body motion planning for humanoid robots—a field demanding solutions to high-dimensional control, stability, and obstacle avoidance. His most cited paper, “Whole-body motion planning for humanoid robots with heuristic search” (2016, 3 citations), addresses these challenges by moving beyond simplistic bounding-box models to develop more efficient, search-based planning algorithms. This contribution is foundational for enabling humanoid robots to navigate complex, real-world environments with fluid and stable movements. While his citation count is modest, Zafar’s work represents a critical step in bridging the gap between theoretical motion planning and practical deployment in robotics. His research is particularly valuable for students and engineers tackling the intricate trade-offs between computational efficiency and physical feasibility in humanoid locomotion. By advancing heuristic search methods, Zafar has helped lay the groundwork for more autonomous and responsive humanoid systems, marking him as a thoughtful contributor to the ongoing evolution of robotic motion intelligence.