Zebing Wang
Papers
1
Total Citations
12
H-Index
1
About
Zebing Wang is a researcher in swarm robotics and distributed intelligence, with a focus on self-organising systems and bio-inspired algorithms. His most-cited work, "A self-organising cooperative hunting by robotic swarm based on particle swarm optimisation localisation" (2015, 12 citations), introduces a novel approach where individual robots, using only local directional information about a moving target, coordinate through particle swarm optimisation (PSO) to achieve collective hunting. This contribution demonstrates how simple, local interactions can enable complex group behaviours without centralised control—a key challenge in swarm robotics. Wang’s research advances the understanding of decentralised decision-making and target localisation in multi-robot systems, with implications for autonomous surveillance, search-and-rescue, and environmental monitoring. His work bridges theoretical PSO models and practical robotic applications, offering scalable solutions for dynamic, uncertain environments. By showing that minimal sensing and communication can yield effective swarm-level outcomes, Wang’s studies provide foundational insights for engineers and researchers developing resilient, adaptive robotic collectives.
Research Focus
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Top Papers
- 1