Papers
25
Total Citations
1,272
H-Index
12
About
Zidong Wang is a versatile researcher whose work spans robotics, control systems, and computational intelligence, with particular expertise in mobile robot path planning, state estimation, and optimization algorithms. His most celebrated contributions lie in intelligent path planning for mobile robots, where he has pioneered the integration of evolutionary algorithms — including Particle Swarm Optimization (PSO) and Genetic Algorithms — with Bezier curve representations to generate smooth, kinematically feasible trajectories. His 2020 paper on an improved PSO algorithm for Bezier-curve-based path planning has garnered over 427 citations, establishing it as a landmark reference in the field. Complementing this work, Wang has made significant contributions to robust filtering and control under real-world imperfections, including his widely cited research on finite-horizon H∞ filtering with missing measurements and quantization effects (226 citations). More recently, he has extended his expertise into contact force estimation for robot manipulators using Gaussian Process-enhanced Kalman filtering and sliding-mode control over fading wireless channels. With a research portfolio spanning over a decade and citations numbering in the thousands, Wang's interdisciplinary contributions have meaningfully advanced both theoretical control frameworks and practical robotics applications.
Research Focus
Key Achievements
Top Papers
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- 4A new genetic algorithm approach to smooth path planning for mobile robots128 citations · 2016
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