Xinghua Lu

Guangdong University of Technology, Guangzhou Huali College

Papers

Total Citations

H-Index

About

Xinghua Lu’s research focuses on intelligent robotics, with particular emphasis on industrial automation, obstacle avoidance algorithms, and route planning optimization. Their work explores how reinforcement learning and parameter optimization techniques can enhance robotic perception and motion control in complex environments. Notable contributions include developing a full freedom pose measurement method for industrial robots using reinforcement learning (5 citations), designing obstacle avoidance algorithms for submarine intelligent robots (3 citations), and proposing an intelligent route planning model based on inertia moment parameter optimization (2 citations). These studies address critical challenges in robotic navigation and control, particularly for underwater and industrial applications. While Lu’s citation counts are modest, their research demonstrates a commitment to advancing autonomous systems through computational intelligence. The submarine robot obstacle avoidance work, published in a coastal engineering context, highlights interdisciplinary applications of robotics. Lu’s contributions provide foundational insights for researchers working on adaptive robotic systems in constrained or dynamic environments, offering practical solutions for improving robot autonomy and operational efficiency.