Papers
3
Total Citations
175
H-Index
3
About
Yibing Cui is a leading researcher at the intersection of swarm intelligence, reinforcement learning, and robotics, with a particular focus on autonomous navigation. Their work centers on enhancing the Artificial Bee Colony (ABC) algorithm—a nature-inspired optimization method—by integrating machine learning and fractional calculus to solve complex, real-world path planning problems. Cui’s most influential contribution is the development of a reinforcement learning-based ABC algorithm, which dramatically improves the efficiency and adaptability of robot path planning in dynamic environments. This seminal work, published in 2022, has already garnered 73 citations, underscoring its impact on the field. Building on this, Cui extended the approach to multi-robot systems, achieving 60 citations for a 2023 study that addresses coordination and collision avoidance among multiple agents. Further innovation includes a fractional-order ABC algorithm, cited 42 times, which introduces memory and hereditary properties into the optimization process, enabling smoother and more precise trajectories. Collectively, these contributions have positioned Cui as a key figure in advancing bio-inspired robotics, offering scalable solutions for autonomous systems in manufacturing, logistics, and exploration.
Research Focus
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