Tao Pang

Institute of Art, Beijing University of Technology

Papers

Total Citations

H-Index

About

Tao Pang is a researcher focused on autonomous robotics, particularly in the areas of search and rescue operations, robot navigation, and environmental mapping in unknown or hazardous settings. His work centers on developing bionic, self-learning algorithms that combine neural network-based mapping with heuristic search methods. Pang’s major contributions include the integration of Growing Self-organizing Maps (GSOM) with A* and Q-Learning algorithms to enable robots to build cognitive maps of unfamiliar environments and plan optimal paths without prior knowledge. He has also advanced monocular vision-based mapping using Dynamic Growing-SOM (DGSOM) algorithms, allowing robots to autonomously construct spatial representations from visual data alone. While his most cited papers each hold 4 citations, their consistent focus on practical, real-world applications—such as search and rescue missions—highlights the niche but critical impact of his work. Pang’s research bridges the gap between theoretical machine learning and field robotics, offering scalable solutions for robots operating in dynamic, unstructured environments.