Muhammad Suhaimi Sulong

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Muhammad Suhaimi Sulong is a robotics researcher whose work focuses on autonomous navigation and path planning, particularly through the advancement of artificial potential field (APF) methods. His major contributions lie in developing more efficient and practical algorithms for robot motion planning that address key limitations of traditional approaches. Sulong’s most cited work, “Improved Potential Field Method for Robot Path Planning with Path Pruning” (2020, 17 citations), introduces a novel technique to eliminate unnecessary path segments, significantly reducing computational complexity while maintaining safety and completeness. His subsequent paper, “Efficient robotic path planning algorithm based on artificial potential field” (2021, 15 citations), further refines these methods by optimizing the trade-off between path length, computational efficiency, and goal reachability. These contributions are vital for real-world applications where robots must navigate dynamic environments quickly and reliably. By enhancing the classic APF framework, Sulong has provided practical solutions that reduce processing time without sacrificing path quality, making his work valuable for researchers and engineers developing autonomous systems in areas such as service robotics, warehouse automation, and unmanned vehicles.