Yuequan Yang

About

Yuequan Yang’s research lies at the intersection of multi-robot systems, reinforcement learning, and intelligent control, with a focus on enabling autonomous robots to operate effectively in unstructured and dynamic environments. His most cited work, “A Distributed Hunting Approach for Multiple Autonomous Robots” (2013, 12 citations), introduces a novel strategy for coordinated hunting using local sensing and effective sectors, allowing robots to collaborate without a central controller. Building on this, his 2012 survey on reinforcement learning for multi-robot systems (5 citations) provides a foundational overview of key algorithms like Q-learning and Dyna, helping to bridge theory and application. Yang has also explored hybrid models for human-robot interaction, designing a chat robot that combines retrieval and generative approaches using LSTM and attention mechanisms (2021, 3 citations). His work on second-order fuzzy control for dynamic obstacle avoidance (2015) and local weighted kNN-TD reinforcement learning for motion control (2012) further demonstrates his commitment to practical, real-time decision-making. Though his citation counts are modest, Yang’s contributions are notable for their breadth—spanning coordination, learning, and interaction—and for addressing core challenges in autonomous robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A Distributed Hunting Approach for Multiple Autonomous Robots

   12 citations · 2013
2. 2
   A survey of reinforcement learning research and its application for multi-robot systems

   5 citations · 2012
3. 3
   A hybrid and regenerative model chat robot based on LSTM and attention model

   3 citations · 2021
4. 4
   A new second-order fuzzy control scheme for dynamic obstacle avoidance

   2 citations · 2015
5. 5
   Research on robot motion control based on local weighted kNN-TD reinforcement learning

   2 citations · 2012

Key Collaborators

Contact & Links