Li Sheng

China University of Petroleum, East China

Papers

Total Citations

136

H-Index

About

Li Sheng is a researcher specializing in autonomous robotics, computational intelligence, and motion planning, with a particular focus on developing sophisticated algorithms that enable mobile robots to navigate complex environments efficiently and safely. His most recognized contribution is a pioneering 2016 study introducing a hybrid approach that integrates genetic algorithms with Bézier curves for smooth path planning in mobile robots, a work that has garnered over 128 citations and established him as a notable voice in the robotics optimization community. This research introduced an innovative grid-based workspace representation that streamlines evolutionary operations, significantly advancing how robots can compute collision-free, smooth trajectories. Building on this foundation, Sheng continued pushing boundaries with a 2019 study proposing a novel adaptive particle swarm optimization (PSO) algorithm, which dynamically evaluates swarm success rates to intelligently guide particle behavior during search, further enhancing global path planning performance in obstacle-rich environments. Together, these works demonstrate a consistent research trajectory centered on applying and refining nature-inspired metaheuristic algorithms to solve real-world robotics challenges, making his contributions valuable reference points for students and researchers working at the intersection of artificial intelligence and autonomous systems.