Muhammad Esmat

King Fahd University of Petroleum and Minerals

Papers

Total Citations

H-Index

About

Muhammad Esmat is a researcher in robotics and artificial intelligence, with a primary focus on solving complex kinematic challenges in manipulator systems. His most cited work, "Solving Inverse Kinematics for 3R Manipulator Using Artificial Neural Networks" (2023), addresses the long-standing Inverse Kinematic Problem (IKP)—a critical issue in robotics that often involves computationally intensive equations and risks of singularity solutions. By leveraging artificial neural networks, Esmat proposes a more efficient and robust alternative to traditional analytical methods, reducing computational time and improving accuracy for three-revolute (3R) manipulators. Although his citation count is currently modest at 2, this work signals a promising contribution to the field of robotic motion planning and control. Esmat’s research bridges the gap between classical robotics theory and modern machine learning techniques, offering practical solutions for real-world automation applications. His work is particularly relevant for students and researchers exploring neural network-based approaches to inverse kinematics, and it lays groundwork for future advancements in adaptive robotic systems.