Runhua Wang

About

Runhua Wang is a leading researcher in robotics, specializing in motion planning and visual servoing for mobile robots. Her work addresses critical challenges in autonomous navigation, particularly ensuring robots can operate safely under real-world constraints. Wang’s most impactful contribution is the development of a virtual-goal-guided RRT (Rapidly-exploring Random Tree) approach for visual servoing, which simultaneously satisfies field-of-view and velocity constraints—a paper cited 51 times. She also advanced path planning with a goal-biased bidirectional RRT algorithm that uses curve-smoothing for seamless trajectory generation (44 citations). Her research extends to handling velocity and acceleration saturation in visual tracking (25 citations) and improving multi-robot path planning efficiency through region heuristics in the RH-ECBS framework. Wang’s work is foundational for autonomous systems requiring precise, constraint-aware navigation, making her a key figure in the field. Her innovative algorithms have been widely adopted, with over 128 total citations, demonstrating significant impact on both theoretical and applied robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Virtual-Goal-Guided RRT for Visual Servoing of Mobile Robots With FOV Constraint

   51 citations · 2021
2. 2
   Goal-biased Bidirectional RRT based on Curve-smoothing

   44 citations · 2019
3. 3
   Visual tracking of mobile robots with both velocity and acceleration saturation constraints

   25 citations · 2020
4. 4
   RH-ECBS: enhanced conflict-based search for MRPP with region heuristics

   4 citations · 2024
5. 5
   Visual Servoing of Mobile Robots with Input Saturation at Kinematic Level

   4 citations · 2017

Key Collaborators

Contact & Links