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About

Juqi Hu is a robotics researcher whose work focuses on the intersection of intelligent perception, autonomous navigation, and adaptive control for robotic systems. Hu’s most cited paper, “A Real-Time Grasping Detection Network Architecture for Various Grasping Scenarios” (2024, 8 citations), introduces an integrated system designed to overcome the challenges posed by diverse object shapes, colors, materials, and poses in robotic grasping. This work addresses a fundamental bottleneck in industrial and service robotics. Complementing this, Hu’s survey “A Path Planning for Mobile Robot Systems” (2023, 8 citations) provides a comprehensive overview of navigation strategies for robots ranging from sweeping to greeting applications, reflecting the rapid expansion of mobile robotics in daily life. In the domain of control theory, Hu proposed an adaptive neural network sliding mode variable structure control for nonholonomic mobile robots (2023, 5 citations), enhancing tracking precision under uncertainty. Further contributions include a survey on modeling and control for flexible systems (2023, 3 citations) and a study on adaptive vibration control for bionic flapping-wing aircraft (2022, 2 citations), showcasing a breadth of expertise from rigid to flexible robotic platforms. With a growing citation footprint, Hu’s work is shaping practical solutions for real-world robotic challenges.