Zhixiong Nan

About

Zhixiong Nan is a researcher advancing the fields of mobile robotics, autonomous navigation, and human-robot interaction. His work focuses on solving complex optimization problems in path planning and enabling robots to anticipate human actions. Nan’s most impactful contribution is the “Global-Local Coupling Two-Stage Path Planning” (CTSP) method, which addresses the nonlinear challenges of mobile robot navigation by combining global optimization with local adjustments—a paper that has garnered 78 citations. He has also developed techniques for predicting short-term next-active-objects using visual attention and hand position (14 citations), and proposed an intention action anticipation model with a guide-feedback loop mechanism (5 citations). In indoor environments, Nan created a straight skeleton-based method for automatically generating hierarchical topological maps (4 citations), reducing computational complexity for real-time navigation. His work extends to all-terrain vehicles (ATVs) navigating complex, unstructured environments containing stairs (2 citations), showcasing his commitment to practical, real-world applications. Through these contributions, Nan is shaping the future of autonomous systems that can perceive, plan, and act in dynamic settings.

Research Focus

Key Achievements

Top Papers

1. 1
   A Global-Local Coupling Two-Stage Path Planning Method for Mobile Robots

   78 citations · 2021
2. 2
   Predicting short-term next-active-object through visual attention and hand position

   14 citations · 2020
3. 3
   Intention action anticipation model with guide-feedback loop mechanism

   5 citations · 2024
4. 4
   Straight Skeleton Based Automatic Generation of Hierarchical Topological Map in Indoor Environment

   4 citations · 2021
5. 5
   ATV Navigation in Complex and Unstructured Environment Containing Stairs

   2 citations · 2020

Key Collaborators

Contact & Links