Jun‐Wei Wang

Stevens Institute of Technology, Beijing Information Science & Technology University, University of Science and Technology Beijing

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About

Jun-Wei Wang is a researcher specializing in autonomous robotics, cooperative control systems, and environmental monitoring technologies. His work sits at the intersection of distributed control theory and practical robotic applications, with a particular focus on using mobile robot networks to address real-world challenges such as environmental pollution tracking. Wang's most influential contribution is his 2019 paper "Dynamic Plume Tracking by Cooperative Robots," which has garnered 50 citations and represents a significant advance in the field. This work, alongside his complementary study on leaderless cooperative control of robotic sensor networks, establishes sophisticated mathematical frameworks using advection-diffusion partial differential equations (PDEs) to model pollutant plume propagation, enabling autonomous robot teams to dynamically track and monitor environmental hazards across multi-dimensional spaces. His earlier 2017 single-robot plume tracking study laid important groundwork through observer-based motion control schemes that preceded these multi-agent approaches. More recently, Wang has expanded his research into construction robotics, applying reinforcement learning to optimize formation control of robotic rollers in cooperative compaction tasks, demonstrating a versatile methodological toolkit. With a career trajectory moving from environmental monitoring toward intelligent construction automation, Wang's research exemplifies how advanced control theory can translate into impactful engineering solutions.