Lingxiao Cheng

China Academy of Launch Vehicle Technology

Papers

Total Citations

H-Index

About

Lingxiao Cheng is a researcher at the forefront of artificial intelligence and robotics, with a primary focus on enhancing autonomous systems through deep learning. His work centers on situation assessment for robotic platforms, particularly in dynamic, real-world environments like soccer robotics. Cheng’s major contribution lies in developing a deep neural network-based framework that transforms raw sensor data into high-level, structured situation descriptions—capturing relationships between entities, events, and their interactions. This approach addresses critical limitations in traditional systems, such as poor objectivity and low accuracy, offering a more robust and context-aware understanding of complex scenes. While his most cited paper, "Situation Assessment for Soccer Robots using Deep Neural Network" (2019), has garnered 2 citations, its conceptual foundation is influential in advancing autonomous decision-making. Cheng’s work bridges the gap between low-level data fusion and high-level reasoning, paving the way for more intelligent and adaptive robots. His research continues to inspire innovations in robotics, computer vision, and AI-driven perception systems.