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About

Ju Hong Park is a pioneering researcher in intelligent robotic systems, with a primary focus on autonomous path planning for robot arms and advanced manufacturing automation. His major contributions lie in developing hybrid AI-driven approaches that integrate Q-learning, neural networks, and computer vision to overcome critical limitations in traditional path planning algorithms—such as high computational costs and unreliable 3D object localization. Park’s most influential work, "A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm" (2021), has garnered 39 citations, while his subsequent study on computer vision-based 3D path planning (2022) has earned 38 citations, underscoring the significant impact of his research on smart manufacturing and Industry 4.0 applications. Notably, Park has also ventured into novel robotic design, co-authoring a paper on a large-scale 3D printing system using a truncated tetrahedral tensegrity robot. His work is essential reading for students and researchers interested in adaptive robotics, reinforcement learning, and the intersection of AI with industrial automation, offering practical solutions for enabling robot arms to operate autonomously in dynamic, obstacle-filled environments.