Junmin Li

About

Junmin Li is a leading researcher in autonomous robotics, specializing in intelligent path planning and localization for mobile and multi-robot systems. Their major contributions lie in developing novel hybrid algorithms that integrate evolutionary computation, neural networks, and fuzzy control to solve complex navigation challenges. Li’s most cited work, “The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm” (2024, 22 citations), introduces a groundbreaking Hybrid Adaptive Genetic Algorithm (HAGA) that dynamically adapts to task hazard levels and complex road environments. This is complemented by their dual-layer symmetric path planning system (2025, 14 citations), which combines an improved neural network with the Dynamic Window Approach for multi-robot coordination. Li has also advanced industrial robotics with a high-precision localization method using improved AMCL and QR code assistance (2025, 5 citations), and pioneered dual-layer fuzzy control with genetic algorithms for safety-critical tasks (2025, 6 citations). Their work consistently bridges theoretical innovation with practical deployment, making significant strides in autonomous navigation for both dynamic outdoor and structured industrial settings.

Research Focus

Key Achievements

Top Papers

1. 1
   The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm in Different Tasks and Complex Road Environments

   22 citations · 2024
2. 2
   A Dual-Layer Symmetric Multi-Robot Path Planning System Based on an Improved Neural Network-DWA Algorithm

   14 citations · 2025
3. 3
   Path Planning of Mobile Robot Based on Dual-Layer Fuzzy Control and Improved Genetic Algorithm

   6 citations · 2025
4. 4
   Research on high-precision localization method for transport robots in industrial environments based on Improved AMCL and QR code assistance

   5 citations · 2025

Key Collaborators

Contact & Links