Max Robotics — Global Service Robot Directory + US Certification

About

Tsai Hong Hong is a robotics and autonomous systems researcher whose work spans computer vision, mobile robotics, sensor calibration, and robot perception. Best known for pioneering research in motion vision, his 1995 paper on using optical flow divergence for real-time obstacle avoidance — now with 170 citations — demonstrated how flow features alone could enable robust robot mobility, a foundational contribution to vision-based navigation. His 2012 overview of robot-sensor calibration methods (90 citations) has become an essential reference for researchers tackling the AX=XB and AX=YB calibration problems, systematically comparing closed-form and iterative solution strategies. Hong's contributions extend to autonomous vehicle navigation, including road detection using fused LADAR and camera data under the U.S. Army's Demo III project, traversability learning for ground vehicles through DARPA's LAGR program, and super-resolution enhancement of flash LADAR imagery. His 2016 survey on mobile manipulator performance measurement (61 citations) helped establish a structured research foundation in a previously underexplored area. Complementing this, his work on sensor ontologies for manufacturing applications reflects a breadth of interest in perception systems across domains. Collectively, his research has meaningfully advanced autonomous robot navigation, multi-sensor fusion, and systematic perception evaluation.