Papers
3
Total Citations
20
H-Index
2
About
Joungmin Park is a rising force in intelligent manufacturing and autonomous robotics, with research that bridges the gap between theoretical reinforcement learning and real-world industrial automation. His work centers on three critical areas: autonomous grasping and manipulation, digital twin integration for flexible manufacturing, and efficient path planning for mobile robots. Park’s most influential contribution is the **GadgetArm** system (2020, 10 citations), which combines automated object recognition with reinforcement learning to enable a 4-DOF robot arm to generate grasps and manipulate arbitrary objects—a key enabler for Industry 4.0’s vision of self-adaptive production lines. He further advanced the field by developing a **digital twin framework** (2023, 9 citations) for plug-and-produce machine tending systems, using the ISO 21919 interface to dramatically simplify the deployment of robot-assisted CNC automation. Most recently, Park introduced an **accelerated block searching approach for A*** (2025), tackling the computational bottlenecks of path planning for autonomous mobile robots in large-scale environments. With a growing citation record, Park is establishing himself as a practical innovator whose work directly addresses the memory and computational constraints that limit industrial robotics adoption.
Research Focus
Key Achievements
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