Papers
4
Total Citations
20
H-Index
3
About
Yijiang Pang is a robotics and autonomous systems researcher whose work sits at the intersection of multi-agent coordination, human-swarm cooperation, and resilient autonomous decision-making. His research tackles the complex challenge of deploying heterogeneous robot teams — combining unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) — in high-stakes real-world environments such as disaster rescue, precision agriculture, and social security operations. Pang's most recognized contribution, "Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming" (2021, 8 citations), demonstrates how reinforcement learning can be leveraged to dynamically adapt robot team configurations to varying environmental demands. A consistent thread across his work is the development of trust-aware frameworks for human-swarm cooperative systems, enabling robot swarms to respond resiliently to emergencies and faults without compromising mission continuity — as reflected in multiple publications exploring reflective control and dynamic task response. With a cumulative citation count of 20 across his key works, Pang's research offers meaningful advances in making autonomous multi-robot systems more adaptive, fault-tolerant, and practically deployable in scenarios where human safety is paramount.
Research Focus
Key Achievements
Top Papers
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