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About

Peng Cui is a robotics researcher whose work centers on autonomous navigation, path planning, and bio-inspired robotic systems. His primary contributions lie in developing algorithms that enable mobile robots to operate safely and efficiently in unknown or obstacle-filled environments. Notably, his 2018 paper on smooth path planning for robot docking, which has garnered 13 citations, introduces a tree-structured heuristic approach to generate collision-free trajectories while satisfying pose constraints—a critical capability for autonomous recharging and data exchange. Complementing this, his 2017 work on reactive path planning (11 citations) addresses the real-time challenges of docking when obstacles appear dynamically. Cui has also advanced the field of biomimetic robotics, as seen in his 2018 study on a semibiomimetic robotic fish, which simplifies mechanical complexity while maintaining dynamic control through Lagrangian modeling. By integrating Dubins curves with rapidly-exploring random trees (RRT) for pose-constrained navigation, his research bridges theoretical path planning with practical robotic applications. With a focused portfolio of work on docking, obstacle avoidance, and underwater robotics, Peng Cui continues to contribute to the development of more autonomous and adaptable robotic systems.