Liangheng Lv

University of Shanghai for Science and Technology

Papers

Total Citations

149

H-Index

About

Dr. Liangheng Lv is a prominent researcher in robotics and artificial intelligence, with a primary focus on intelligent navigation and path planning. His most impactful work, "Path Planning via an Improved DQN-Based Learning Policy" (2019), has garnered 149 citations, establishing him as a key contributor to the integration of reinforcement learning in autonomous systems. Dr. Lv’s major contribution lies in advancing deep Q-network (DQN) algorithms to enhance the efficiency and adaptability of robotic navigation, enabling machines to learn complex path-planning strategies from diverse, real-world experiences rather than relying on static rules. His research addresses a critical challenge in robotics—how to mimic human learning processes to improve decision-making in dynamic environments. By refining reinforcement learning frameworks, Dr. Lv has helped bridge the gap between theoretical AI and practical robotic applications, influencing fields from autonomous vehicles to industrial automation. His work is widely recognized for its practical impact, offering scalable solutions that improve both the safety and performance of autonomous navigation systems. Dr. Lv continues to push the boundaries of intelligent robotics, making him a vital figure for students and researchers exploring the future of AI-driven mobility.