Qiang Yu

China University of Mining and Technology, Institute for Infocomm Research, Shanghai University of Engineering Science

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About

Qiang Yu is a researcher at the forefront of autonomous navigation and neuromorphic computing. His work centers on developing intelligent path planning algorithms that enable mobile robots and manipulators to operate efficiently without reliance on pre-mapped environments. Yu’s most influential contribution, the "Convolutionally Evaluated Gradient First Search" algorithm (2021, 13 citations), introduces a novel approach to real-time, collision-free navigation that bypasses the need for global maps—a critical advancement for dynamic, unstructured settings. He has also made significant strides in neuromorphic cognitive systems, proposing a learning- and memory-centered framework (2017, 12 citations) that bridges biological inspiration with robotic decision-making. His comprehensive review of global and local path planning algorithms (2024, 5 citations) serves as a key reference for the field, synthesizing decades of progress. Most recently, Yu has tackled the challenge of manipulator obstacle avoidance using an improved Rapidly-exploring Random Tree (RRT) algorithm (2025, 4 citations), demonstrating continued innovation in safe, adaptive motion planning. With a growing citation impact and a focus on practical, map-free autonomy, Yu’s work is shaping the next generation of intelligent, perceptive robots.