Yaoming Zhuang

About

Yaoming Zhuang’s research lies at the intersection of artificial intelligence, robotics, and wireless sensor networks, with a focus on solving real-world monitoring and navigation challenges. His most impactful work introduces a deep learning framework using a structured space model to detect small objects in complex underwater environments—a critical tool for marine ecosystem monitoring that balances accuracy with real-time performance, earning 13 citations since 2025. In the domain of wireless sensor and robot networks (WSRNs), Zhuang has made foundational contributions to event-driven deployment and coverage repair. His 2019 paper on mobile robot-based repair strategies for event coverage holes, along with his 2020 collaborative neural network algorithm for adaptive sensor deployment, each garnered 5 citations, addressing the need for flexible surveillance in constrained, hazardous settings. More recently, Zhuang tackled autonomous patrol robot navigation with an improved A* path planning method (2024), enhancing multi-point route efficiency. Collectively, his work demonstrates a sustained commitment to bridging algorithmic innovation with practical deployment in dynamic environments, establishing him as a key contributor to intelligent monitoring and robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   A deep learning framework based on structured space model for detecting small objects in complex underwater environments

   13 citations · 2025
2. 2
   The Repair Strategy for Event Coverage Holes Based on Mobile Robots in Wireless Sensor and Robot Networks

   5 citations · 2019
3. 3
   Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

   5 citations · 2020
4. 4
   Research on Path Planning Method for Autonomous Patrol Robots

   3 citations · 2024

Key Collaborators

Contact & Links