Canglong Liu

Chongqing Institute of Green and Intelligent Technology

Papers

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About

Dr. Canglong Liu is a leading researcher in the intersection of robotics, computer vision, and deep learning, with a primary focus on autonomous navigation and intelligent control systems. His most influential work centers on developing end-to-end learning models for mobile robot obstacle avoidance, where he pioneered the use of Convolutional Neural Networks (CNNs) to process raw visual data directly for real-time decision-making in indoor environments. His seminal 2017 paper, which has garnered 26 citations, introduced a novel CNN-based vision model that enables robots to navigate unknown or known spaces without explicit mapping, marking a significant advance in autonomous exploration. This work has been foundational for subsequent studies in deep reinforcement learning for robotics and has practical implications for service robots and autonomous vehicles. Dr. Liu’s contributions bridge the gap between theoretical deep learning architectures and tangible robotic applications, demonstrating how raw image inputs can be transformed into actionable movement commands. His research continues to push the boundaries of intelligent systems, making him a respected voice in the robotics and AI communities.