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About

Maoyong Cao is a robotics and control systems researcher whose work centers on mobile robot path planning, trajectory tracking, and intelligent optimization algorithms. His most significant contributions lie in developing novel approaches to smooth path planning for mobile robots, leveraging Bézier curve geometry combined with advanced optimization techniques such as particle swarm optimization (PSO). His 2021 paper introducing a quartic Bézier transition curve with an improved PSO algorithm has garnered 126 citations, establishing him as a leading voice in computationally efficient, geometrically smooth motion planning. Complementing this, his work on continuous-curvature path planning using parametric cubic Bézier curves has accumulated 40 citations, further demonstrating his sustained focus on practical constraints in real-world robot navigation. Beyond path planning, Cao has made notable strides in control theory, proposing a hybrid backstepping and fractional-order PID controller for differential-drive robot trajectory tracking, earning 50 citations since 2022. His earlier investigations into sensor applications, including linear CCD-based path recognition systems, reflect a career-long commitment to bridging hardware sensing and intelligent robot control. Collectively, his research provides both theoretical rigor and practical solutions for autonomous mobile robot systems.