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About

Xingcheng Pu is a robotics and computational intelligence researcher whose work centers on autonomous navigation, path planning, and the development of bio-inspired optimization algorithms. With a focused and productive research agenda, Pu has made significant contributions to solving one of robotics' most fundamental challenges: enabling mobile robots to navigate efficiently and safely through complex environments. Pu's most influential work introduced an Improved Artificial Fish Swarm Algorithm (IAFSA) for robot path planning, garnering 39 citations since 2016 and establishing a foundation for subsequent research in swarm intelligence applications. Building on this, Pu has systematically advanced ant colony optimization techniques, addressing persistent limitations such as slow convergence and susceptibility to local optima. Notable contributions include a hybrid IACO-SFLA algorithm combining improved ant colony optimization with the shuffled frog leaping algorithm, as well as extensions into three-dimensional path planning and multi-robot, multi-objective scenarios. Collectively accumulating nearly 100 citations, Pu's body of work demonstrates a consistent drive to bridge theoretical algorithmic improvements with practical robotic applications. Their research offers valuable tools for engineers and researchers working on autonomous systems, warehouse robotics, and intelligent navigation technologies.