Jinjia Guo
Papers
2
Total Citations
44
H-Index
2
About
Jinjia Guo is a leading researcher in intelligent robotics and autonomous systems, with a focus on multi-robot coordination, real-time visual perception, and industrial manipulation. Their work bridges the gap between advanced neural network control and practical automation challenges. Guo’s most cited paper, "A varying-parameter complementary neural network for multi-robot tracking and formation via model predictive control" (2024, 31 citations), introduces a novel neural network framework that enables multiple robots to dynamically track targets and maintain formation—a critical advancement for collaborative robotics in manufacturing and logistics. Another influential contribution, "A Real-Time 3-D Visual Detection-Based Soft Wire Avoidance Scheme for Industrial Robot Manipulators" (2023, 13 citations), addresses a persistent industrial problem: the interference of soft wires with robot end-effector tasks. By proposing a real-time 3D visual detection and avoidance scheme, Guo provides a practical solution that enhances operational reliability and safety in automated production. These works demonstrate Guo’s ability to combine theoretical innovation with real-world applicability, earning recognition for advancing both multi-agent systems and industrial robotics. Their research continues to shape how robots perceive and interact with complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
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