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About

Qipeng Li is a leading researcher in autonomous mobile robotics, specializing in path planning, navigation, and intelligent control for complex environments. Their major contributions center on developing hybrid optimization algorithms that significantly improve the efficiency and accuracy of robot motion. Notably, Li pioneered a jump point search improved ant colony optimization (ACO) algorithm for mobile robot path planning (27 citations), which reduces path turns and enhances convergence speed—a critical advancement for real-world deployment. They further advanced the field with a motion planning method for car-like robots navigating dynamic obstacles (22 citations), addressing the challenge of incompletely constrained systems in cluttered settings. Li’s work on 3D navigation systems for automated guided vehicles (AGVs) in smart factories (15 citations) and a novel Lidar-IMU fusion navigation system (LIFNS) marks a shift from traditional 2D plane navigation to robust 3D solutions, enabling AGVs to operate in increasingly complex industrial environments. Their recent research includes an island-type improved ACO algorithm (6 citations) and a calibration method using multilayer perceptron and genetic algorithms for visual-guided robots (1 citation). With over 70 total citations, Li’s innovations are pivotal for advancing autonomous navigation in smart manufacturing and robotics.