Zike Wang

About

Zike Wang is a pioneering researcher in multi-robot systems and vision-based autonomous navigation, with a career spanning two decades. His foundational work introduced the "Constrain and Move" strategy for distributed object transportation, enabling multiple robots to collaboratively move objects along desired paths—a key contribution to cooperative robotics. Wang also made early advances in outdoor robot self-navigation, developing color image processing algorithms robust to varying illumination, critical for autonomous road-following on ill-structured forest and mountain roads. His recent research integrates convolutional neural networks (CNNs) for low-cost, monocular depth estimation, facilitating collision avoidance in multi-robot teams. Wang’s most cited paper (19 citations) and his LOGUE architecture for task and behavior transmission among autonomous robots demonstrate his sustained impact on practical, scalable multi-agent systems. His work bridges classical control strategies with modern deep learning, offering accessible solutions for real-world deployment.

Research Focus

Key Achievements

Top Papers

1. 1
   Distributed object transportation on a desired path based on Constrain and Move strategy

   19 citations · 2004
2. 2
   On robot self-navigation in outdoor environments by color image processing

   15 citations · 2004
3. 3
   A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network

   4 citations · 2023
4. 4
   LOGUE: an architecture for task and behavior object transmission among multiple autonomous robots

   4 citations · 2003

Key Collaborators

Contact & Links