Papers
16
Total Citations
1,107
H-Index
11
About
Baoye Song is a prominent researcher specializing in mobile robotics, with a particular focus on path planning, trajectory optimization, and intelligent control systems. Over more than a decade of prolific work, Song has made substantial contributions to the development of smooth and efficient path planning algorithms for mobile robots, consistently leveraging nature-inspired optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Ant Colony Optimization (ACO) in combination with sophisticated mathematical tools like Bézier curves. Song's most influential work, "An Improved PSO Algorithm for Smooth Path Planning of Mobile Robots Using Continuous High-Degree Bezier Curve" (2020), has garnered over 427 citations, establishing it as a landmark reference in the field. His sustained body of research addresses real-world challenges including kinematic constraints, continuous-curvature requirements, and dynamic environments, reflecting a deep commitment to practical applicability. More recently, Song has expanded into trajectory tracking control, proposing a hybrid backstepping and fractional-order PID controller, and has tackled specialized domains such as coal mine robotics. With a cumulative citation count exceeding 1,000 across his top works, Song's research offers students and engineers a rich, rigorous foundation for advancing autonomous mobile robot navigation.
Research Focus
Key Achievements
Top Papers
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- 3A new genetic algorithm approach to smooth path planning for mobile robots128 citations · 2016
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