Papers
5
Total Citations
42
H-Index
3
About
Suyu Wang is a robotics and automation researcher whose work centers on autonomous tunneling systems, robotic roadheaders, and intelligent trajectory planning. His most significant contributions lie in developing kinematic models and cutting trajectory planning methodologies that enable tunneling robots to autonomously navigate and excavate complex underground cross-sections — a challenging problem at the intersection of robotics, mining engineering, and computational intelligence. Wang's 2018 papers on spatially-arbitrary cross-section cutting and complex-composition section planning represent his most impactful work, garnering 19 and 17 citations respectively, and collectively advancing the field of robotized tunneling by integrating multi-sensor data to identify geological features such as dirt bands and adapt cutting paths accordingly. His 2019 study further refined these approaches using improved Particle Swarm Optimization, demonstrating a commitment to applying metaheuristic algorithms to real-world engineering challenges. More recently, Wang has expanded his research horizon toward bio-inspired robot learning, exploring fusion policy transfer learning for obstacle avoidance — signaling a broader interest in generalizable autonomous systems. His body of work reflects a consistent focus on bridging theoretical robotics with practical underground automation, making meaningful contributions to the development of safer, more intelligent mining and tunneling technologies.
Research Focus
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