Zeying Wang
Papers
2
Total Citations
31
H-Index
2
About
Zeying Wang is a pioneering researcher in the field of multi-robot and swarm-robot systems, with a primary focus on advancing path planning and reinforcement learning algorithms. Her work addresses critical challenges in autonomous navigation, particularly the optimization of coordination and decision-making in uncertain, dynamic environments. Wang’s most influential contribution is her 2013 paper on "The optimization of path planning for multi-robot system using Boltzmann Policy based Q-learning algorithm," which has garnered 23 citations. This study introduced a novel integration of Boltzmann exploration strategies with Q-learning to enhance path efficiency and convergence in multi-robot collaboration. Building on this, her 2013 work on "Adaptive reinforcement Q-Learning algorithm for swarm-robot system using pheromone mechanism" (8 citations) tackled the combinatorial explosion and slow learning issues inherent in continuous state-action spaces. By merging ant colony optimization’s pheromone-based communication with adaptive Q-learning, Wang proposed a scalable framework that improves swarm intelligence and real-time adaptability. Her research bridges theoretical reinforcement learning with practical robotics, offering impactful solutions for autonomous systems in logistics, exploration, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2