Mao Tan
Papers
1
Total Citations
29
H-Index
1
About
Mao Tan is a leading researcher in robotics and intelligent optimization, whose work focuses on autonomous navigation and path planning in complex, dynamic environments. His major contributions lie in developing hybrid algorithms that integrate advanced mathematical methods with swarm intelligence to solve real-world robotic challenges. Notably, his 2020 paper, "A cubic spline method combining improved particle swarm optimization for robot path planning in dynamic uncertain environment," has garnered 29 citations, addressing a critical factory inspection scenario. In this work, Tan introduced an inertial positioning strategy that enables robots to navigate efficiently among moving targets and both static and dynamic obstacles, significantly enhancing safety and precision in unpredictable settings. His research bridges theoretical optimization and practical robotics, offering robust solutions for industrial automation. With a growing citation impact, Tan’s work is recognized for its innovation in merging cubic spline interpolation with particle swarm optimization, setting a benchmark for adaptive robot control. His achievements underscore a commitment to advancing autonomous systems, making him a key figure for students and researchers exploring intelligent robotics and real-time decision-making under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1