Max Robotics — Global Service Robot Directory + US Certification

About

Yurong Liu is a researcher whose work sits at the intersection of intelligent robotics, optimization algorithms, and control systems theory. Best known for contributions to robot path planning, Liu has developed innovative computational approaches that enable autonomous robots to navigate complex, obstacle-laden environments more effectively. Their most influential work, "Path planning for intelligent robot based on switching local evolutionary PSO algorithm" (2016, 90 citations), introduced a hybrid optimization framework combining particle swarm optimization with differential evolution and non-homogeneous Markov chains — a notable advancement in three-dimensional robotic navigation. Building on this foundation, Liu further refined path planning through genetic algorithms and an improved bidirectional rapidly exploring random tree initialization method (2017, 40 citations), enhancing performance in dynamic environments. Their application of deep learning, specifically convolutional neural networks, to biomimetic robot navigation (2016, 21 citations) demonstrates a forward-thinking embrace of modern AI techniques. More recently, Liu has extended their expertise into control theory, contributing to H∞-based sampled-data control for fuzzy Markov jump systems (2021, 20 citations). With a cumulative body of work exceeding 170 citations, Liu represents a versatile and impactful voice in intelligent systems and robotics research.