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About

Junghee Park is a robotics researcher whose work focuses on intelligent navigation, collision anticipation, and stealth-based motion planning for mobile robots operating in dynamic and adversarial environments. Park’s major contributions include developing an analytic collision anticipation technology that predicts when and where collisions will occur in configuration time space by considering agents’ future behavior—a foundational approach cited 15 times. This work moves beyond reactive solutions to enable proactive, safe navigation. Park also pioneered roadmap-based stealth navigation for intercepting invaders, allowing robots to move covertly by hiding behind obstacles while actively predicting and planning against evasive targets (8 citations). Further innovations include long-term stealth navigation in monitored security zones, sampling-based planning for maximum-margin obstacle avoidance under uncertainty (5 citations), and near time-optimal motion planning for moving obstacle avoidance with a three-phase decomposition strategy (5 citations). Park’s research is notable for integrating prediction, planning, and execution to address real-world challenges in security, surveillance, and autonomous navigation. With a total of 39 citations across key papers, Park’s work has laid important groundwork for safe, intelligent robot motion in complex, uncertain, and adversarial settings.