Lasmadi Lasmadi

Universitas Teknologi Yogyakarta, Institute of Technology and Business

Papers

Total Citations

H-Index

About

Lasmadi Lasmadi is a researcher specializing in mobile robotics, sensor fusion, and control systems, with a particular focus on estimation techniques for autonomous navigation. His major contributions center on applying Kalman Filter variants—especially the Unscented Kalman Filter (UKF)—to improve localization accuracy for mobile robots operating in unknown environments. His 2019 paper on mobile robot localization via UKF (10 citations) addresses the critical challenge of position and heading estimation without prior environmental knowledge, offering a robust alternative to the Extended Kalman Filter. In related work, he developed an IMU-based rotation angle estimation method using Kalman Filtering (9 citations), targeting applications in UAVs, spacecraft, and underwater vehicles where sensor noise often degrades accuracy. Lasmadi also contributed to robotics education and simulation through his 2019 modeling and simulation of a 3-DOF robotic arm using V-REP (5 citations), demonstrating practical approaches to industrial automation. His research bridges theoretical estimation algorithms with real-world robotic systems, making his work valuable for students and engineers developing autonomous platforms. With cumulative citations reflecting growing interest in his sensor fusion and localization methods, Lasmadi continues to advance the field of mobile robotics in Indonesia and beyond.