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About

Guangqiang Li is a researcher whose work sits at the intersection of robotics, artificial intelligence, and optimization, with a particular focus on solving complex path planning and layout problems. His most influential contribution is a deep-learning real-time visual SLAM system, which integrates a multi-task feature extraction network with self-supervised feature points, a paper that has garnered 77 citations and demonstrates his ability to advance autonomous navigation. Li has also made significant strides in swarm intelligence, notably improving the Artificial Fish Swarm Algorithm for robot path planning—a challenging area in robotics—and developing a hybrid particle swarm optimization-genetic algorithm for NP-complete layout problems, which have applications in spacecraft module design, shipping, and vehicle routing. His more recent work extends to the Harris Hawk Optimization Algorithm for global path planning, employing a coarse-to-fine planning strategy. While his citation counts reflect a growing impact, Li’s contributions are notable for their practical focus on real-world robotic systems and their integration of bio-inspired algorithms with deep learning, positioning him as a researcher bridging theoretical optimization and applied robotics.