Liming Xie

Xi'an University of Technology, Lanzhou University of Technology

Papers

Total Citations

H-Index

About

Liming Xie’s research focuses on robotics and computational kinematics, particularly the development of optimization algorithms for solving complex inverse kinematics and acceleration problems in under-actuated robotic systems. Their major contribution lies in pioneering hybrid genetic algorithms to address the computational challenges of inverse acceleration for robots with fewer than six degrees of freedom (DOFs)—a critical issue in simplifying real-time control for industrial and service robots. By integrating genetic algorithms with traditional numerical methods, Xie’s work reduces reliance on complex Jacobian and second-order influence coefficient matrices, offering more efficient solutions for robot motion planning. Despite modest citation counts (e.g., 4 citations for their 2008 paper on inverse acceleration), this work represents an early attempt to apply evolutionary computation to robotic dynamics, influencing subsequent research in hybrid optimization. Notably, a 2009 paper on 5R robot inverse kinematics was later retracted, underscoring the challenges of early-stage algorithmic validation. Xie’s contributions highlight the ongoing struggle to balance computational efficiency with accuracy in robotics, providing a foundation for students and researchers exploring genetic algorithm applications in mechanical systems.