Fan Ren

About

Fan Ren is a robotics researcher whose work focuses on intelligent path planning, robotic grasping, and dynamic target tracking for service and industrial robots. Ren’s most influential contribution is a global path planning method for mobile service robots that integrates ant colony optimization with fuzzy control, achieving 79 citations by addressing slow convergence through an initial pheromone distribution guided by critical obstacle influence factors. Ren has also advanced robotic manipulation with a grasping method based on an improved Gaussian mixture model that incorporates Bayesian inference for more robust training. In dynamic environments, Ren developed a re-entry path planning algorithm using a dynamic Inver-Over evolutionary approach for complete coverage in cleaning robots, and a target tracking method for grasping moving objects that employs an affine group improved Gaussian resampling particle filter. Additional work includes a self-adjusting factor fuzzy control algorithm for seam tracking in welding robots, enhancing adaptability and dynamic performance. Ren’s research demonstrates a consistent focus on improving autonomy, efficiency, and real-time performance in robotic systems, with applications spanning service robotics to industrial automation.

Research Focus

Key Achievements

Top Papers

1. 1
   A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

   79 citations · 2021
2. 2
   A method for robotic grasping based on improved Gaussian mixture model

   7 citations · 2019
3. 3
   A Re-Entry Path Planning Method for Service Robots Based on Dynamic Inver-Over Evolutionary Algorithm

   7 citations · 2019
4. 4
   A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

   5 citations · 2021
5. 5
   A robot method based on self-adjusting factor fuzzy control algorithm for the seam tracking

   3 citations · 2019

Key Collaborators

Contact & Links