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About

Swee Ho Tang is a robotics researcher whose work focuses on mobile robot navigation, path planning, and autonomous systems. His key research areas include wavefront-based path planning algorithms, automated guided vehicle (AGV) design, sensor data fusion, and simultaneous localization and mapping (SLAM). Tang’s most cited work, “Comparison between Normal Waveform and Modified Wavefront Path Planning Algorithm for Mobile Robot” (2014, 5 citations), addresses the computational inefficiency of traditional wavefront expansion in large-scale environments, proposing a modified approach to reduce processing time while maintaining collision-free navigation. He also contributed to AGV structural optimization through finite element analysis, enhancing mechanical stress tolerance for industrial payloads. In “Low Cost Sensor Data Fusion in Omnidirectional Mobile Robot Feedback System” (2014, 2 citations), Tang tackled the challenge of stable navigation in dynamic factory settings by integrating low-cost sensors. His work on SLAM using the Unscented Kalman Filter (2016, 2 citations) improved localization accuracy for unmanned ground vehicles by compensating for observation outliers. Though his citation counts are modest, Tang’s research addresses practical, industry-relevant problems in mobile robotics, bridging the gap between theoretical algorithms and real-world deployment in manufacturing and logistics environments.